Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) United States License.

Motivation

This case study will introduce the topic of multicolinearity. We will do so by showcasing a real world example where multicolinearity in part resulted in historically contriversial and conflicting findings about the influence of the adoption of right-to-carry (RTC) concealed handgun laws on violent crime rates in the United States.

We will focus on two articles:

The first analysis by Lott and Mustard published in 1996 suggests that RTC laws reduce violent crime. Lott authored a book extending these findings in 1998 called More Guns, Less Crime.

The second analysis is a recent article by Donohue, et al. published in 2017 that suggests that RTC laws increase violent crime. Donohue has also published previous articles with titles such as “Shooting down the”More Guns, Less Crime" Hypothesis

This has been a controversial topic as many other articles also had conflicting results. See here for a list of studies.

The Donohue, et al. article discusses how there are many other important methodolical aspects besides multicolinearity that could account for the historically conflicting results in these previous papers.

In fact, nearly every aspect of the data analysis process was different between the Donohue, et al. analysis and the Lott and Mustard analysis.

However, we will focus particularly on multicolinearity and we will explore how it can influence linear regression analyses and result in different conclusions.

This analysis will demonstrate how methodological details can be critically influential for our overall conclusions and can result in important policy related consequences. This article will provide a basis for the motivation.

John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. Journal of Empirical Legal Studies, 16,2 (2019).

David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. Coase-Sandor Institute for Law & Economics Working Paper No. 41, (1996).

Here you can see the differences in the data used in the featured RTC articles:

We will perform analyses similar to those in these articles, however we will not try to recreate them, instead we will simplify our analysis to allow us to focus on multicolinearity.

Therefore we will use a subset of the listed explanatory variables and they will be consistent for both analyses that we will perform, with the exception that one analysis will have 6 demographic variables like the analysis in the Donohue, et al. article and the other will have 36 demogrpahic variables like the analysis in the Lott and Mustard article.

Main Question

Our main question:

How does the inclusion of different numbers of age groups influence the results of an analysis of right to carry laws and violence rates?

Learning Objectives

Statistical Learning Objectives:

what multicollinearity is and how it can influence linear regression coefficients
how to look for the presence of multicollinarity and determine the severity
the difference between multicollinearity and correlation

Data science Learning Objectives:

data import of many different file types with special cases (readr, readxl, pdftools)
joining data from multiple sources (dplyr)
working with character strings (stringr)
data comparisons (dplyr and janitor)
reshaping data into different formats (tidyr)
visualizations (ggplot2)
perform iterative simulations (rsample)

We will especially focus on using packages and functions from the Tidyverse, such as dplyr and ggplot2. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.

Context

So what exactly is a right-to-carry law?

It is a law thatspecifies if and how citizens are allowed to have a firearm on their person or nearby (for example in the citizen’s car) in public.

The Second Amendment to the United States Constitution guarantees the right to “keep and bear arms”. The amendment was ratified in 1791 as part of the Bill of Rights.

However, there are no federal laws about carrying firearms in public.

These laws are created and enforced at the state level. Sates vary greatly in their laws about the right to carry firearms. Some require extensive effort to obtain a permit to legally carry a firearm, while other states require very minimal effort to legally carry a firearm.

According to Wikipedia about the history of right-to-carry policies in the United States:

Public perception on concealed carry vs open carry has largely flipped. In the early days of the United States, open carrying of firearms, long guns and revolvers was a common and well-accepted practice. Seeing guns carried openly was not considered to be any cause for alarm. Therefore, anyone who would carry a firearm but attempt to conceal it was considered to have something to hide, and presumed to be a criminal. For this reason, concealed carry was denounced as a detestable practice in the early days of the United States.

Concealed weapons bans were passed in Kentucky and Louisiana in 1813. (In those days open carry of weapons for self-defense was considered acceptable; concealed carry was denounced as the practice of criminals.) By 1859, Indiana, Tennessee, Virginia, Alabama, and Ohio had followed suit. By the end of the nineteenth century, similar laws were passed in places such as Texas, Florida, and Oklahoma, which protected some gun rights in their state constitutions. Before the mid 1900s, most U.S. states had passed concealed carry laws rather than banning weapons completely. Until the late 1990s, many Southern states were either “No-Issue” or “Restrictive May-Issue”. Since then, these states have largely enacted “Shall-Issue” licensing laws, with numerous states legalizing “Unrestricted concealed carry”.

See here for more information.

Here are the general categories of Right to Carry Laws:

source

You can see that none of the fifty states have no-issue laws currently (the gray category), meaning that all states allow the right to carry firearms at least in some way, however the level of restrictions is dramatically different from one state to another.

Here you can see how these laws have changed over time around the country:

There is variation from state to state even within the same general category:

For example here are the current carry laws in Idaho which is considered an “Unrestricted - no permit required” state:

Idaho permits the open carrying of firearms.

Idaho law permits both residents and non-residents who are at least 18 years old to carry concealed weapons, without a carry license, outside the limits of or confines of any city, provided the person is not otherwise disqualified from being issued a license to carry.

A person may also carry concealed weapons on or about his or her person, without a license, in the person’s own place of abode or fixed place of business, on property in which the person has any ownership or leasehold interest, or on private property where the person has permission to carry from any person who has an ownership or leasehold interest in that property.

State law also allows any resident of Idaho or a current member of the armed forces of the United States to carry a concealed handgun without a license to carry, provided the person is over 18 years old and not disqualified from being issued a license to carry concealed weapons under state law. An amendment to state law that takes effect on July 1, 2020 changes the reference in the above law from “a resident of Idaho” to “any citizen of the United States.”

And here are the current carry laws in Arizona which is also considered an “Unrestricted - no permit required” state:

Arizona respects the right of law abiding citizens to openly carry a handgun.

Any person 21 years of age or older, who is not prohibited possessor, may carry a weapon openly or concealed without the need for a license. Any person carrying without a license must acknowledge and comply with the demands of a law enforcement officer when asked if he/she is carrying a concealed deadly weapon, if the officer has initiated an “investigation” such as a traffic stop.

Notice that citizens in Idaho only need to be 18 to carry a firearm, whereas they must be 21 in Arizona.

In contrast here is an example of current carry laws in Maryland which is considered a “Rights Restricted-Very Limited Issue” state:

Carrying and Transportation in Vehicles It is unlawful for any person without a permit to wear or carry a handgun, openly or concealed, upon or about his person. It is also unlawful for any person to knowingly transport a handgun in any vehicle traveling on public roads, highways, waterways or airways, or upon roads or parking lots generally used by the public. This does not apply to any person wearing, carrying or transporting a handgun within the confines of real estate owned or leased by him, or on which he resides, or within the confines of a business establishment owned or leased by him.

Permit To Carry Application for a permit to carry a handgun is made to the Secretary of State Police. In addition to the printed application form, the applicant should submit a notarized letter stating the reasons why he is applying for a permit.

avocado….Right to carry and covid masks?

Limitations

There are some important considerations regarding this data analysis to keep in mind:

We do not use all of the data used by either the Lott and Mustard or Donohue, et al. analyses, nor do we perform the same analysis of each article. We instead perform a much simpler analysis with less variables for the purposes of illustration of the concept of multicollinearity and its influence on regression coefficients, not to reproduce either analysis.
Our analysis accounts for either the adoption or lack of adoption of a permissive right-to-carry law in each state, but does not acount for differences in the level of permissiveness of the laws.

Recall that these are the categories of right to carry laws: States with laws of the category rights restricted - very limited issue (red) are considered as not having a permissive right-to-carry law. Recall that no states currently have a rights infringed/non-issue law.

States of all other categories (shall issue, discretionary/reasonable issue, and no permit required) (all shades of blue) are considered the same in our analysis, as having a permissive right-to-carry law.

Because our analysis is an oversimplification, our analysis should not be used for determining policy changes, instead we suggest that users consult with a specialist.

We would also like to note that…AVOCADO It is important that we do not treat race as an objective measure. Despite this, it can be used to advance scientific inquiry. For more information on this topic, we have included a link to a paper on the use of race as a measure in epidemiology.

We will begin by loading the packages that we will need:

library(here)
library(readxl)
library(readr)
library(pdftools)
library(dplyr)
library(magrittr)
library(tidyr)
library(stringr)
library(purrr)
library(forcats)
library(tibble)
library(car) # vif function
library(plm) # fixed effect model, linear regression
library(broom) # tidy output
library(cowplot) # to produce plot of plots 
library(GGally)
library(ggrepel)
library(scales)
library(latex2exp)
library(viridis)
library(ggcorrplot)
library(rsample)

set.seed(999)

Package	Use
here	to easily load and save data
readr	to import the CSV file data
[car]	to calculate vif values
[purrr]	to combine multiple tibbles within a list of tibbles
[forcats]	to collapse levels of factors into more summarised versions

The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.

What are the data?

Below is a table from the Donohue, et al. paper that shows the data used in both analyses, where DAW stands for Donohue, et al. and LM stands for Lott and Mustard.

We will be using a subset of these variables, which are highlighted in green:

Data Import

Demographic and population data

To obtain information about age, sex, and race, and overall population we will use US Census Bureau data, just like both of the articles. The cesnus data is available for different time spans. Here are the links for the years used in our analysis. We will use data from 1977 to 2010.

Data	Link
years 1977 to 1979	link
years 1980 to 1989	link * county data was used for this decade which also has state information
years 1990 to 1999	link
years 2000 to 2010	link technical documentation

To import the data we will use the read_csv() function of the readr package for the csv files. In some decades, there are separate files for each year, we will read each of these together using the base list.files() function to get all of the names for each file and then the map() function of the purrr package to apply the read_csv() function on all of the file paths in the list created by list.files(). For years that are txt files we will use read_table2() also fo the readr package. The read_table2() function, unlike the read_table(), allows for any number of whitespace characters between columns, and the lines can be of different lengths.

AVOCADO I am a bit confused about the last decade… it’s only one file but it seems to need map…

dem_77_79 <- read_csv("docs/Demographics/Decade_1970/pe-19.csv", skip = 5)

dem_80_89 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1980/",
                  pattern = "*.csv",
                  full.names = TRUE) %>% 
  map(~read_csv(., skip=5))

dem_90_99 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1990/",
                  pattern = "*.txt",
                  full.names = TRUE) %>% 
  map(~read_table2(., skip = 14))


dem_00_10_2 <- read_csv("docs/Demographics/Decade_2000/st-est00int-alldata.csv")

dem_00_10 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_2000/",
                  pattern = "*.csv",
                   full.names = TRUE) %>% 
   map(~read_csv(.))

head(dem_00_10)

[[1]]
# A tibble: 62,244 x 21
   REGION DIVISION STATE NAME    SEX ORIGIN  RACE AGEGRP ESTIMATESBASE20…
    <dbl>    <dbl> <dbl> <chr> <dbl>  <dbl> <dbl>  <dbl>            <dbl>
 1      0        0     0 Unit…     0      0     0      0        281424600
 2      0        0     0 Unit…     0      0     0      1         19176154
 3      0        0     0 Unit…     0      0     0      2         20549855
 4      0        0     0 Unit…     0      0     0      3         20528425
 5      0        0     0 Unit…     0      0     0      4         20218782
 6      0        0     0 Unit…     0      0     0      5         18962964
 7      0        0     0 Unit…     0      0     0      6         19381792
 8      0        0     0 Unit…     0      0     0      7         20511067
 9      0        0     0 Unit…     0      0     0      8         22707390
10      0        0     0 Unit…     0      0     0      9         22442442
# … with 62,234 more rows, and 12 more variables: POPESTIMATE2000 <dbl>,
#   POPESTIMATE2001 <dbl>, POPESTIMATE2002 <dbl>, POPESTIMATE2003 <dbl>,
#   POPESTIMATE2004 <dbl>, POPESTIMATE2005 <dbl>, POPESTIMATE2006 <dbl>,
#   POPESTIMATE2007 <dbl>, POPESTIMATE2008 <dbl>, POPESTIMATE2009 <dbl>,
#   CENSUS2010POP <dbl>, POPESTIMATE2010 <dbl>

Notice that the STATE variable for the demographic data is numeric. That is because it is encoded by Federal Information Processing Standard (FIPS) state codes{target="_blank". Thus we also need to import data about FIPS encoding so that we can identify what data corresponds to what state.

State FIPS codes

The following data was downloaded from the US Census Bureau.

To import the data we will use the read_xls() function of the readxl package. Since the first five lines of this excel is information about the source of the data and when it was released, we need to skip importing these lines using the skip argument so that the data has the same number of columns for each row.

knitr::include_graphics(here("img", "FIPS.png"))

STATE_FIPS <- read_xls("docs/State_FIPS_codes/state-geocodes-v2014.xls", skip = 5)
(STATE_FIPS)

# A tibble: 64 x 4
   Region Division `State\n(FIPS)` Name                    
   <chr>  <chr>    <chr>           <chr>                   
 1 1      0        00              Northeast Region        
 2 1      1        00              New England Division    
 3 1      1        09              Connecticut             
 4 1      1        23              Maine                   
 5 1      1        25              Massachusetts           
 6 1      1        33              New Hampshire           
 7 1      1        44              Rhode Island            
 8 1      1        50              Vermont                 
 9 1      2        00              Middle Atlantic Division
10 1      2        34              New Jersey              
# … with 54 more rows

Police staffing data

The following data was downloaded from the Federal Bureau of Investigation.

The read_csv() function of the readr package guesses what the class is for each variable, but sometimes it makes mistakes. It is good to specify the class for variables if you know them. We know that we want the variables about male and female counts to be numeric. We can specify that using the col_types = argument. See here and here for more information.

ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv")
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                    col_types = cols(male_total_ct = "n",
                                     female_total_ct = "n"))

ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                   col_types =  cols(male_total_ct = col_double(),
                                   female_total_ct = col_double()))
head(ps_data)

# A tibble: 6 x 21
  data_year ori   pub_agency_name pub_agency_unit state_abbr division_name
      <dbl> <chr> <chr>           <chr>           <chr>      <chr>        
1      1960 AK02… Alcohol Bevera… <NA>            AK         Pacific      
2      1960 AL00… Homewood        <NA>            AL         East South C…
3      1960 AL01… Coffeeville     <NA>            AL         East South C…
4      1960 AL01… Coffee          <NA>            AL         East South C…
5      1960 AL02… Mentone         <NA>            AL         East South C…
6      1960 AL03… Greensboro      <NA>            AL         East South C…
# … with 15 more variables: region_name <chr>, county_name <chr>,
#   agency_type_name <chr>, population_group_desc <chr>, population <dbl>,
#   male_officer_ct <dbl>, male_civilian_ct <dbl>, male_total_ct <dbl>,
#   female_officer_ct <lgl>, female_civilian_ct <lgl>, female_total_ct <dbl>,
#   officer_ct <lgl>, civilian_ct <lgl>, total_pe_ct <lgl>,
#   pe_ct_per_1000 <lgl>

Unemplyment data

The following data was downloaded from the U.S. Bureau of Labor Statistics.

There are excel files for each state. As you can see, there are many rows to skip to make sure that there are the same number of columns for each row. We can also see that the state name is located in a couple of the first rows.

knitr::include_graphics(here("img", "Unemp.png"))

We can also see that here if we just try to read in the files directly.

ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(.))
      
head(ue_rate_data)[1]

[[1]]
# A tibble: 55 x 14
   `Local Area Une… ...2  ...3  ...4  ...5  ...6  ...7  ...8  ...9  ...10 ...11
   <chr>            <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
 1 Original Data V… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 2 <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 3 Series Id:       LAUS… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 4 Not Seasonally … <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 5 Area:            Alab… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 6 Area Type:       Stat… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 7 State/Region/Di… Alab… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 8 Measure:         unem… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 9 Years:           1977… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
10 <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
# … with 45 more rows, and 3 more variables: ...12 <chr>, ...13 <chr>,
#   ...14 <chr>

So now we will skip the first 10 lines. And also create a names tibble that contains only the cell with the state information.

 ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(., skip = 10))
  
head(ue_rate_data[1])

[[1]]
# A tibble: 44 x 14
    Year   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  1977   7.5   9     7.7   7.2   6.8   8.6   8     7.8   6.7   6.3   6.3   6  
 2  1978   7.1   6.9   6.2   5.4   5.1   6.9   6.7   6.7   6.5   6.3   6.3   6.5
 3  1979   6.7   7.5   6.9   6.6   6.4   8.4   7.7   7.8   7.1   7.2   6.9   6.7
 4  1980   7.7   7.8   7.4   7.4   8.4   9.7  10.4  10.3   9.3   9.6   9.4   9  
 5  1981  10    10.3   9.5   9.1   9.4  11.1  10.4  10.9  10.8  11.7  11.5  11.8
 6  1982  13.2  13.2  12.9  12.6  12.8  14.5  14.7  14.8  14.7  15.1  15.4  15.3
 7  1983  16    16    14.5  13.7  13.3  14.6  13.9  13.8  13.2  12.8  12.1  11.8
 8  1984  12.5  12.4  11.4  10.8  10.1  11.3  11.5  11.3  10.8  10.2   9.7  10.1
 9  1985  10.7  10.5   9.8   8.7   8.4   9.6   9.2   8.8   8.6   8.6   8.4   8.7
10  1986   9.3  10.4  10.1   9.4   9.4  10.5   9.7   9.6   9.7   9.7   9.6   9  
# … with 34 more rows, and 1 more variable: Annual <dbl>

To get the state name for each file using the map() function to perform functions across all of the files, we will specifically import only a small range of cells using the range = argument and then grab the cell that has state information based on it’s location within the range of cells imported using c() and then use the base unlist() function to unlist the list that this creates.

ue_rate_names <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>%
  map(~read_xlsx(., range = "B4:B6")) %>%
  map(., c(1,2)) %>%
  unlist()

ue_rate_names

 [1] "Alabama"              "Alaska"               "Arizona"             
 [4] "Arkansas"             "California"           "Colorado"            
 [7] "Connecticut"          "Delaware"             "District of Columbia"
[10] "Florida"              "Georgia"              "Hawaii"              
[13] "Idaho"                "Illinois"             "Indiana"             
[16] "Iowa"                 "Kansas"               "Kentucky"            
[19] "Louisiana"            "Maine"                "Maryland"            
[22] "Massachusetts"        "Michigan"             "Minnesota"           
[25] "Mississippi"          "Missouri"             "Montana"             
[28] "Nebraska"             "Nevada"               "New Hampshire"       
[31] "New Jersey"           "New Mexico"           "New York"            
[34] "North Carolina"       "North Dakota"         "Ohio"                
[37] "Oklahoma"             "Oregon"               "Pennsylvania"        
[40] "Rhode Island"         "South Carolina"       "South Dakota"        
[43] "Tennessee"            "Texas"                "Utah"                
[46] "Vermont"              "Virginia"             "Washington"          
[49] "West Virginia"        "Wisconsin"            "Wyoming"

Now we will make these values the names of the different tibbles within ue_rate_data.

names(ue_rate_data) <- ue_rate_names

Poverty data

Extracted from Table 21 from US Census Bureau Poverty Data

AVOCado strange issue

#**persistent warning from unknown origin** https://community.rstudio.com/t/persistent-unknown-or-uninitialised-column-warnings/64879

#solution to above is alledgedly: "In any case the suggested approach is to initialize the column"


poverty_rate_data <- read_xls("docs/Poverty/hstpov21.xls", skip=2) #This may cause initialization issue, not easily reproducible (even after restarting R)

head(poverty_rate_data)

# A tibble: 6 x 6
  `NOTE: Number in thousa… ...2  ...3   ...4         ...5         ...6          
  <chr>                    <chr> <chr>  <chr>        <chr>        <chr>         
1 2018                     <NA>  <NA>    <NA>        <NA>          <NA>         
2 STATE                    Total Number "Standard\n… Percent      "Standard\ner…
3 Alabama                  4877  779    "65"         16           "1.3"         
4 Alaska                   720   94     "9"          13.1         "1.2"         
5 Arizona                  7241  929    "80"         12.80000000… "1.1000000000…
6 Arkansas                 2912  462    "38"         15.9         "1.3"

We can see that this will require some wranlging to make the data more usable.

Violent crime

Violent crime data was obtained from here This data is a bit trickier because of spaces and / in the column names, thus the read_lines() function of the readr package works better than the read_csv() function.

knitr::include_graphics(here("img", "crime.png"))

crime_data <- read_lines("docs/Crime/CrimeStatebyState.csv", skip = 2, skip_empty_rows = TRUE)
head(crime_data)

[1] "Estimated crime in Alabama"                                                                                                           
[2] "\n,,National or state crime,,,,,,,"                                                                                                   
[3] "\n,,Violent crime,,,,,,,"                                                                                                             
[4] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"
[5] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "                                                      
[6] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "

We can see that this data will also require some wranlging to make it more usable.

Right-to-carry data

This data is extracted from table in Donohue paper {target="_blank"}. We will use the function pdf_text() of the pdftools package to import the pdf document.

if(!file.exists(here("docs", "w23510.pdf"))){
  url <- "https://www.nber.org/papers/w23510.pdf"
  utils::download.file(url, here("docs", "w23510.pdf"))
}

DAWpaper <- pdf_text(here("docs", "w23510.pdf"))

head(DAWpaper[1])

[1] "                                NBER WORKING PAPER SERIES\n                      RIGHT-TO-CARRY LAWS AND VIOLENT CRIME:\n              A COMPREHENSIVE ASSESSMENT USING PANEL DATA AND\n                    A STATE-LEVEL SYNTHETIC CONTROL ANALYSIS\n                                          John J. Donohue\n                                            Abhay Aneja\n                                           Kyle D. Weber\n                                         Working Paper 23510\n                                 http://www.nber.org/papers/w23510\n                      NATIONAL BUREAU OF ECONOMIC RESEARCH\n                                     1050 Massachusetts Avenue\n                                        Cambridge, MA 02138\n                                 June 2017, Revised November 2018\nPreviously circulated as \"Right-to-Carry Laws and Violent Crime: A Comprehensive Assessment\nUsing Panel Data and a State-Level Synthetic Controls Analysis.\" We thank Dan Ho, Stefano\nDellaVigna, Rob Tibshirani, Trevor Hastie, StefanWager, Jeff Strnad, and participants at the\n2011 Conference of Empirical Legal Studies (CELS), 2012 American Law and Economics\nAssociation (ALEA) Annual Meeting, 2013 Canadian Law and Economics Association (CLEA)\nAnnual Meeting, 2015 NBER Summer Institute (Crime), and the Stanford Law School faculty\nworkshop for their comments and helpful suggestions. Financial support was provided by\nStanford Law School. We are indebted to Alberto Abadie, Alexis Diamond, and Jens\nHainmueller for their work developing the synthetic control algorithm and programming the Stata\nmodule used in this paper and for their helpful comments. The authors would also like to thank\nAlex Albright, Andrew Baker, Jacob Dorn, Bhargav Gopal, Crystal Huang, Mira Korb, Haksoo\nLee, Isaac Rabbani, Akshay Rao, Vikram Rao, Henrik Sachs and Sidharth Sah who provided\nexcellent research assistance, as well as Addis O’Connor and Alex Chekholko at the Research\nComputing division of Stanford’s Information Technology Services for their technical support.\nThe views expressed herein are those of the author and do not necessarily reflect the views of the\nNational Bureau of Economic Research.\nNBER working papers are circulated for discussion and comment purposes. They have not been\npeer-reviewed or been subject to the review by the NBER Board of Directors that accompanies\nofficial NBER publications.\n© 2017 by John J. Donohue, Abhay Aneja, and Kyle D. Weber. All rights reserved. Short\nsections of text, not to exceed two paragraphs, may be quoted without explicit permission\nprovided that full credit, including © notice, is given to the source.\n"

Again, this data will also require quite a bit of wrangling.

Data Wrangling

State FIPS codes

Let’s first take a look at our state FIPS data to see if it needs any cleaning or reshaping. We should start with this data, becuase we will need to use it to wrangle some of the other data.

head(STATE_FIPS)

# A tibble: 6 x 4
  Region Division `State\n(FIPS)` Name                
  <chr>  <chr>    <chr>           <chr>               
1 1      0        00              Northeast Region    
2 1      1        00              New England Division
3 1      1        09              Connecticut         
4 1      1        23              Maine               
5 1      1        25              Massachusetts       
6 1      1        33              New Hampshire

We only need the last two columns, but we might want to rename them. The Name variable is vague. The variable with the FIPS code is called State\n(FIPS). To get rid of the new line in this variable name and to change the Name variable to something more informative, we will use the rename() function of the dplyr package. To use this function, we need to list the new name first followed by = and then the existing variable. We can rename multiple variables at the same time by using a comma to separate the variables we are renaming. We will use the select() function also of the dplyr package just to keep these variables, and we will filter out the rows with FIPS values of 00 with the filter() function, agian also part of the dplyr package. we will specify that we want STATEFP values that are not equal to 00 by using this operator: !=. We will also use the double pipe operator %<>% of the magrittr package which allows us to use data as iuput and then reassign it after we peform sum functions using it.

STATE_FIPS %<>% 
dplyr::rename( STATEFP = `State\n(FIPS)`,
                 STATE = Name) %>%
    dplyr::select(STATEFP, STATE) %>%
    dplyr::filter(STATEFP != "00")

STATE_FIPS

# A tibble: 51 x 2
   STATEFP STATE        
   <chr>   <chr>        
 1 09      Connecticut  
 2 23      Maine        
 3 25      Massachusetts
 4 33      New Hampshire
 5 44      Rhode Island 
 6 50      Vermont      
 7 34      New Jersey   
 8 36      New York     
 9 42      Pennsylvania 
10 17      Illinois     
# … with 41 more rows

Demographic and population data

Click here to see detailed information about how the demogrphic data was wrangled

1977-1979

Now let’s take a look at our demographic data across the decades that we wish to study. If you have very wide data (meaning it has many columns), one way to view the data so that you can see all of the columns at the same time is to use the glimpse() function of the dplyr package.

Taking a look at the first decade of data, we can see that the Race/Sex Indicator contains two types of data, the race and the sex. This does not follow the tidy data philosophy, where each cell of a tibble should only contain one piece of information. Typically one might think of using the separate() function of the tidyr package to split this variable into two. However, one of the race values is Other races and since this also has a space, this makes separating this data more tricky.

Instead we will use the str_extract() function of the stringr package and the mutate() function of the dplyr package. The “mutate()” will allow us to create new variables, and “str_extract()” function will allow us to match specific patterns and pull out matches to those patterns. Therefore, if the Race/Sex Indicator value is Other races male and if we extract patterns matching either "male" or "female" which we can specify like this pattern = "male|female" then, the value will be male.

First we need to rename the Race/Sex Indicator varaible to not have spaces so that it is compatible with the str_extract() function.

We also want to rename a couple of variables to be simpler and filter the data to only include the years of the data we are interested in, as well as remove some variables that we dont need like the FIPS State Code. We can remove variables by using the select() function with a - minus sign in front of the variable we wish to remove.

dplyr::glimpse(dem_77_79)

Rows: 3,060
Columns: 22
$ `Year of Estimate`   <dbl> 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, …
$ `FIPS State Code`    <chr> "01", "01", "01", "01", "01", "01", "02", "02", …
$ `State Name`         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ `Race/Sex Indicator` <chr> "White male", "White female", "Black male", "Bla…
$ `Under 5 years`      <dbl> 105856, 100613, 47403, 47079, 244, 250, 12382, 1…
$ `5 to 9 years`       <dbl> 120876, 115194, 55443, 54851, 255, 251, 13888, 1…
$ `10 to 14 years`     <dbl> 129091, 122352, 60427, 60065, 253, 245, 13255, 1…
$ `15 to 19 years`     <dbl> 119500, 116107, 52921, 55144, 281, 254, 11179, 9…
$ `20 to 24 years`     <dbl> 103665, 108513, 29948, 35165, 413, 331, 20237, 1…
$ `25 to 29 years`     <dbl> 86538, 88359, 19535, 23662, 239, 302, 12538, 107…
$ `30 to 34 years`     <dbl> 74452, 77595, 17196, 22021, 236, 284, 10331, 865…
$ `35 to 39 years`     <dbl> 71511, 74941, 16654, 22248, 161, 279, 9548, 7510…
$ `40 to 44 years`     <dbl> 75242, 78908, 17564, 24249, 127, 253, 8282, 6353…
$ `45 to 49 years`     <dbl> 73874, 78589, 18186, 23028, 108, 148, 6995, 5820…
$ `50 to 54 years`     <dbl> 68048, 72481, 17618, 22104, 95, 100, 5609, 4494,…
$ `55 to 59 years`     <dbl> 61071, 67699, 18118, 21909, 88, 93, 4029, 2986, …
$ `60 to 64 years`     <dbl> 52361, 61065, 16456, 20068, 69, 94, 2392, 1830, …
$ `65 to 69 years`     <dbl> 38977, 49685, 14498, 19364, 54, 73, 1292, 965, 2…
$ `70 to 74 years`     <dbl> 26767, 37227, 9541, 12509, 70, 66, 602, 496, 8, …
$ `75 to 79 years`     <dbl> 17504, 27163, 6030, 8291, 31, 52, 326, 305, 1, 5…
$ `80 to 84 years`     <dbl> 9937, 16470, 3485, 5031, 37, 30, 211, 186, 4, 5,…
$ `85 years and over`  <dbl> 5616, 10445, 2448, 4035, 76, 29, 143, 126, 19, 4…

dem_77_79 <- dem_77_79 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select(-`FIPS State Code`, -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`,
        "STATE" = `State Name`) %>%
  filter(YEAR %in% 1977:1979)

glimpse(dem_77_79)

Rows: 918
Columns: 22
$ YEAR                <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1…
$ STATE               <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alab…
$ `Under 5 years`     <dbl> 98814, 94595, 46201, 45784, 590, 621, 14316, 1353…
$ `5 to 9 years`      <dbl> 113365, 107395, 50097, 49329, 672, 660, 14621, 13…
$ `10 to 14 years`    <dbl> 123107, 116182, 54925, 53955, 677, 653, 14795, 13…
$ `15 to 19 years`    <dbl> 135343, 130433, 58468, 59926, 674, 605, 15207, 13…
$ `20 to 24 years`    <dbl> 126053, 125352, 43898, 51433, 722, 773, 20106, 16…
$ `25 to 29 years`    <dbl> 111547, 112471, 31014, 36648, 638, 835, 20444, 18…
$ `30 to 34 years`    <dbl> 100674, 101543, 22528, 26694, 571, 766, 17514, 15…
$ `35 to 39 years`    <dbl> 81038, 83369, 17473, 22213, 498, 586, 13098, 1069…
$ `40 to 44 years`    <dbl> 75042, 77793, 16446, 22146, 356, 479, 10067, 7935…
$ `45 to 49 years`    <dbl> 76296, 79753, 16578, 22576, 295, 432, 8460, 6848,…
$ `50 to 54 years`    <dbl> 74844, 81079, 17117, 23028, 206, 326, 7268, 5914,…
$ `55 to 59 years`    <dbl> 67785, 75905, 16437, 21435, 166, 213, 5398, 4485,…
$ `60 to 64 years`    <dbl> 58853, 69406, 16276, 21075, 145, 174, 3349, 2708,…
$ `65 to 69 years`    <dbl> 48848, 62430, 15837, 21126, 107, 173, 1714, 1468,…
$ `70 to 74 years`    <dbl> 34475, 50075, 11450, 16028, 90, 138, 915, 928, 22…
$ `75 to 79 years`    <dbl> 20977, 34027, 7601, 10825, 53, 106, 500, 493, 10,…
$ `80 to 84 years`    <dbl> 10831, 21483, 3896, 6272, 25, 49, 237, 268, 4, 7,…
$ `85 years and over` <dbl> 6683, 15729, 2667, 5426, 33, 41, 153, 211, 11, 6,…
$ SEX                 <chr> "male", "female", "male", "female", "male", "fema…
$ RACE                <chr> "White", "White", "Black", "Black", "Other", "Oth…

That’s looking pretty good! We also want to take all the age group variabels and make one variable that is the age group name and one that is the value of the population count for that age group. To do this we will use the pivot_longer() function of the tidyr package. To use this function, we need to use the cols argument to indicate which columns we want to pivot. We also name the new variables we will create with the names_to and values_to arguments. The names_to will be the name of the variable that will identify each age group and values_to will be the name of the variable that contains the corresponding population values.

dem_77_79 <- dem_77_79 %>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP")

glimpse(dem_77_79)

Rows: 16,524
Columns: 6
$ YEAR      <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977,…
$ STATE     <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ SEX       <chr> "male", "male", "male", "male", "male", "male", "male", "ma…
$ RACE      <chr> "White", "White", "White", "White", "White", "White", "Whit…
$ AGE_GROUP <chr> "Under 5 years", "5 to 9 years", "10 to 14 years", "15 to 1…
$ SUB_POP   <dbl> 98814, 113365, 123107, 135343, 126053, 111547, 100674, 8103…

We also want to get data about the total population for the state for each year.

To do so we can sum all the values for the SUB_POP variable that we just created. To do this we can use the group_by and summarise() functions of the dplyr package. The group_by() function specifies how we want to calculate our sum, that we would like to calculate it for each year and each state individually. Thus, all the values that have the same STATE and YEAR values will be summed together, rather than summing using all of the values in the SUB_POP variable. The .groups argument allows us to remove the grouping after we peform the calculation with summarise().

pop_77_79 <- dem_77_79 %>%
  group_by(YEAR, STATE) %>%
  summarise("TOT_POP" = sum(SUB_POP), .groups = "drop") 

pop_77_79

# A tibble: 153 x 3
    YEAR STATE                 TOT_POP
   <dbl> <chr>                   <dbl>
 1  1977 Alabama               3782571
 2  1977 Alaska                 397220
 3  1977 Arizona               2427296
 4  1977 Arkansas              2207195
 5  1977 California           22350332
 6  1977 Colorado              2696179
 7  1977 Connecticut           3088745
 8  1977 Delaware               594815
 9  1977 District of Columbia   681766
10  1977 Florida               8888806
# … with 143 more rows

Now we will add the population value to the demographic tibble using the left_join() function of the dplyr package. It is imporant that we specify how this should be done, that the YEAR and STATE variable vlaues should match eachother. This will place the dem_77_79 variables to the left of the pop_77_79 data.

dem_77_79 <- dem_77_79 %>%
  left_join(pop_77_79, by = c("YEAR","STATE"))

dem_77_79

# A tibble: 16,524 x 7
    YEAR STATE   SEX   RACE  AGE_GROUP      SUB_POP TOT_POP
   <dbl> <chr>   <chr> <chr> <chr>            <dbl>   <dbl>
 1  1977 Alabama male  White Under 5 years    98814 3782571
 2  1977 Alabama male  White 5 to 9 years    113365 3782571
 3  1977 Alabama male  White 10 to 14 years  123107 3782571
 4  1977 Alabama male  White 15 to 19 years  135343 3782571
 5  1977 Alabama male  White 20 to 24 years  126053 3782571
 6  1977 Alabama male  White 25 to 29 years  111547 3782571
 7  1977 Alabama male  White 30 to 34 years  100674 3782571
 8  1977 Alabama male  White 35 to 39 years   81038 3782571
 9  1977 Alabama male  White 40 to 44 years   75042 3782571
10  1977 Alabama male  White 45 to 49 years   76296 3782571
# … with 16,514 more rows

We will also calculate the percentage that each group makes up of the total population, by dividing the SUB_POP by the TOT_POP and multiplying by 100 using the mutate() function. we will also remove the other population variables.

dem_77_79 %<>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  select(-SUB_POP, -TOT_POP)

dem_77_79

# A tibble: 16,524 x 6
    YEAR STATE   SEX   RACE  AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <chr>                 <dbl>
 1  1977 Alabama male  White Under 5 years          2.61
 2  1977 Alabama male  White 5 to 9 years           3.00
 3  1977 Alabama male  White 10 to 14 years         3.25
 4  1977 Alabama male  White 15 to 19 years         3.58
 5  1977 Alabama male  White 20 to 24 years         3.33
 6  1977 Alabama male  White 25 to 29 years         2.95
 7  1977 Alabama male  White 30 to 34 years         2.66
 8  1977 Alabama male  White 35 to 39 years         2.14
 9  1977 Alabama male  White 40 to 44 years         1.98
10  1977 Alabama male  White 45 to 49 years         2.02
# … with 16,514 more rows

It is important to make sure that we have the total values we would expect. We have two levels of SEX, three levels of Race, three levels of YEAR, eighteen levels of AGE_GROUP, and fifty one levels of STATE. If we multiply this together we get 16,524 which is the same as the number of rows in our final dem_77_79 data. Looks good!

Also Let’s make the values of the SEX variable capatalized so that they match the other values of the other variables like RACE etc. This will help us to keep consistent values across the different years as we wrangle the data for the other decades. To do so we will use the str_to_title() function of the stringr package. We need to use the pull() function to get the values of SEX out of dem_77_79. Once we make them captialized they are then reasigned to the SEX variable.

dem_77_79 %<>%
  mutate(SEX = str_to_title(pull(dem_77_79, SEX)))

# This can also be done line this:
dem_77_79 %<>%
  mutate(SEX = str_to_title(pull(., SEX)))

1980-1989

For this decade each year is a separate tibble and they are combined as a list.

class(dem_80_89)

[1] "list"

So the first thing we need to do is combine each tibble of the list together. We can do that using the bind_rows() function of dplyr which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the map_df() function of the purrr package to allow us to do this across each tibble in our list.

dem_80_89 <- dem_80_89 %>%
  map_df(bind_rows)

glimpse(dem_80_89 )

Rows: 188,460
Columns: 21
$ `Year of Estimate`            <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ `FIPS State and County Codes` <chr> "01001", "01001", "01001", "01001", "01…
$ `Race/Sex Indicator`          <chr> "White male", "White female", "Black ma…
$ `Under 5 years`               <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 6…
$ `5 to 9 years`                <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, …
$ `10 to 14 years`              <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614…
$ `15 to 19 years`              <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841…
$ `20 to 24 years`              <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, …
$ `25 to 29 years`              <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, …
$ `30 to 34 years`              <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, …
$ `35 to 39 years`              <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202,…
$ `40 to 44 years`              <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, …
$ `45 to 49 years`              <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, …
$ `50 to 54 years`              <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 2…
$ `55 to 59 years`              <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 1…
$ `60 to 64 years`              <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 1…
$ `65 to 69 years`              <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 1…
$ `70 to 74 years`              <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 16…
$ `75 to 79 years`              <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 1…
$ `80 to 84 years`              <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67…
$ `85 years and over`           <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65,…

Great! Now our data is all together.

Now we will wrangle the data similarly to the previous decade.

dem_80_89 <- dem_80_89 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select( -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`)
         
glimpse(dem_80_89)

Rows: 188,460
Columns: 22
$ YEAR                          <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ `FIPS State and County Codes` <chr> "01001", "01001", "01001", "01001", "01…
$ `Under 5 years`               <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 6…
$ `5 to 9 years`                <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, …
$ `10 to 14 years`              <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614…
$ `15 to 19 years`              <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841…
$ `20 to 24 years`              <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, …
$ `25 to 29 years`              <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, …
$ `30 to 34 years`              <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, …
$ `35 to 39 years`              <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202,…
$ `40 to 44 years`              <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, …
$ `45 to 49 years`              <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, …
$ `50 to 54 years`              <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 2…
$ `55 to 59 years`              <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 1…
$ `60 to 64 years`              <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 1…
$ `65 to 69 years`              <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 1…
$ `70 to 74 years`              <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 16…
$ `75 to 79 years`              <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 1…
$ `80 to 84 years`              <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67…
$ `85 years and over`           <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65,…
$ SEX                           <chr> "male", "female", "male", "female", "ma…
$ RACE                          <chr> "White", "White", "Black", "Black", "Ot…

Notice that this time the state information is based on the numeric FIPS value. We want only the first two values, as the rest indicate the county. We can use the str_sub() function of the stringr package for this. We will specify that we want to start at the first position and end at the second. Just like str_extract() we need to rename this variable first so that it is compatible.

dem_80_89 %<>%
rename("STATEFP_temp" = "FIPS State and County Codes") %>%
mutate(STATEFP = str_sub(STATEFP_temp, start = 1, end = 2)) %>%
    left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

glimpse(dem_80_89)

Rows: 188,460
Columns: 23
$ YEAR                <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1…
$ STATEFP_temp        <chr> "01001", "01001", "01001", "01001", "01001", "010…
$ `Under 5 years`     <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 672, 645, 3…
$ `5 to 9 years`      <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, 740, 680, …
$ `10 to 14 years`    <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614, 644, 670…
$ `15 to 19 years`    <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841, 711, 762…
$ `20 to 24 years`    <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, 516, 601, …
$ `25 to 29 years`    <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, 414, 469, …
$ `30 to 34 years`    <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, 303, 352, …
$ `35 to 39 years`    <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202, 224, 260,…
$ `40 to 44 years`    <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, 206, 288, …
$ `45 to 49 years`    <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, 219, 236, …
$ `50 to 54 years`    <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 203, 261, 7…
$ `55 to 59 years`    <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 178, 219, 8…
$ `60 to 64 years`    <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 171, 209, 8…
$ `65 to 69 years`    <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 170, 232, 6…
$ `70 to 74 years`    <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 164, 182, 4,…
$ `75 to 79 years`    <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 129, 3, 6, …
$ `80 to 84 years`    <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67, 1, 2, 56…
$ `85 years and over` <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65, 1, 1, 30,…
$ SEX                 <chr> "male", "female", "male", "female", "male", "fema…
$ RACE                <chr> "White", "White", "Black", "Black", "Other", "Oth…
$ STATE               <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alab…

dem_80_89 %<>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP_temp") %>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups="drop")

dem_80_89

# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP      SEX    RACE  SUB_POP
   <dbl> <chr>   <chr>          <chr>  <chr>   <dbl>
 1  1980 Alabama 10 to 14 years female Black   50108
 2  1980 Alabama 10 to 14 years female Other     805
 3  1980 Alabama 10 to 14 years female White  109066
 4  1980 Alabama 10 to 14 years male   Black   50768
 5  1980 Alabama 10 to 14 years male   Other     826
 6  1980 Alabama 10 to 14 years male   White  115988
 7  1980 Alabama 15 to 19 years female Black   58428
 8  1980 Alabama 15 to 19 years female Other     743
 9  1980 Alabama 15 to 19 years female White  126783
10  1980 Alabama 15 to 19 years male   Black   56808
# … with 55,070 more rows

pop_80_89 <- dem_80_89 %>%
  group_by(YEAR, STATE) %>%
  summarise("TOT_POP" = sum(SUB_POP), .groups = "drop") 


dem_80_89 <- dem_80_89 %>%
  left_join(pop_80_89, by = c("YEAR","STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_80_89

# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP      SEX    RACE  PERC_SUB_POP
   <dbl> <chr>   <chr>          <chr>  <chr>        <dbl>
 1  1980 Alabama 10 to 14 years female Black       1.28  
 2  1980 Alabama 10 to 14 years female Other       0.0206
 3  1980 Alabama 10 to 14 years female White       2.80  
 4  1980 Alabama 10 to 14 years male   Black       1.30  
 5  1980 Alabama 10 to 14 years male   Other       0.0212
 6  1980 Alabama 10 to 14 years male   White       2.97  
 7  1980 Alabama 15 to 19 years female Black       1.50  
 8  1980 Alabama 15 to 19 years female Other       0.0191
 9  1980 Alabama 15 to 19 years female White       3.25  
10  1980 Alabama 15 to 19 years male   Black       1.46  
# … with 55,070 more rows

Just like with the data from the 70s we will also change the values for SEX to be capitalized.

dem_80_89 %<>%
  mutate(SEX = str_to_title(pull(., SEX)))

Again, it is important to make sure that we have the total values we would expect. This time we have: two levels of SEX, three levels of Race, ten levels of YEAR, eighteen levels of AGE_GROUP, and fifty one levels of STATE.

If we multiply these together we get 55,080, which is the same as the number of rows of the final dem_80_89 data. Looks good!

1990-1999

Just like the 80s we need to combine the data across the files:

dem_90_99 <- dem_90_99 %>%
  map_df(bind_rows)

glimpse(dem_90_99)

Rows: 43,870
Columns: 19
$ Year     <dbl> NA, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 19…
$ e        <chr> NA, "01", "01", "01", "01", "01", "01", "01", "01", "01", "0…
$ Age      <dbl> NA, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
$ Male     <dbl> NA, 20406, 19393, 18990, 19246, 19502, 19560, 19091, 19605, …
$ Female   <dbl> NA, 19101, 18114, 18043, 17786, 18366, 18386, 18047, 18316, …
$ Male_1   <dbl> NA, 9794, 9475, 9097, 9002, 9076, 9169, 8919, 9219, 9247, 10…
$ Female_1 <dbl> NA, 9414, 9247, 8837, 8701, 8989, 9093, 8736, 9192, 9108, 97…
$ Male_2   <dbl> NA, 103, 87, 97, 94, 108, 128, 160, 178, 166, 205, 194, 179,…
$ Female_2 <dbl> NA, 90, 93, 100, 115, 114, 130, 134, 162, 155, 193, 185, 202…
$ Male_3   <dbl> NA, 192, 146, 175, 150, 168, 170, 183, 171, 136, 177, 169, 1…
$ Female_3 <dbl> NA, 170, 182, 160, 157, 178, 158, 173, 177, 185, 179, 171, 1…
$ Male_4   <dbl> NA, 223, 190, 198, 186, 190, 210, 188, 178, 182, 221, 194, 1…
$ Female_4 <dbl> NA, 220, 196, 173, 191, 190, 170, 172, 179, 173, 166, 175, 1…
$ Male_5   <dbl> NA, 47, 41, 32, 35, 36, 30, 28, 27, 29, 32, 31, 33, 34, 32, …
$ Female_5 <dbl> NA, 45, 47, 41, 30, 26, 37, 23, 35, 31, 28, 38, 22, 39, 29, …
$ Male_6   <dbl> NA, 1, 2, 1, 9, 5, 8, 2, 4, 6, 6, 0, 1, 9, 6, 7, 5, 2, 2, 4,…
$ Female_6 <dbl> NA, 8, 0, 2, 1, 4, 5, 3, 4, 4, 3, 4, 2, 2, 7, 0, 2, 2, 1, 6,…
$ Male_7   <dbl> NA, 5, 7, 2, 3, 5, 11, 2, 7, 12, 10, 7, 5, 6, 5, 6, 6, 2, 11…
$ Female_7 <dbl> NA, 5, 5, 5, 3, 14, 6, 7, 6, 3, 11, 5, 5, 7, 8, 6, 6, 7, 3, …

For this decade the column names can’t all be imported in a simple way from the table, so they need to be recoded.

Here is what the data looks like before importing:

So, first using the base colnames() function we change the names of the column names.

colnames(dem_90_99) <- c("YEAR",
                         "STATEFP",
                         "Age",
                         "NH_W_M",
                         "NH_W_F",
                         "NH_B_M",
                         "NH_B_F",
                         "NH_AIAN_M",
                         "NH_AIAN_F",
                         "NH_API_M",
                         "NH_API_F",
                         "H_W_M",
                         "H_W_F",
                         "H_B_M",
                         "H_B_F",
                         "H_AIAN_M",
                         "H_AIAN_F",
                         "H_API_M",
                         "H_API_F")

glimpse(dem_90_99)

Rows: 43,870
Columns: 19
$ YEAR      <dbl> NA, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1…
$ STATEFP   <chr> NA, "01", "01", "01", "01", "01", "01", "01", "01", "01", "…
$ Age       <dbl> NA, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ NH_W_M    <dbl> NA, 20406, 19393, 18990, 19246, 19502, 19560, 19091, 19605,…
$ NH_W_F    <dbl> NA, 19101, 18114, 18043, 17786, 18366, 18386, 18047, 18316,…
$ NH_B_M    <dbl> NA, 9794, 9475, 9097, 9002, 9076, 9169, 8919, 9219, 9247, 1…
$ NH_B_F    <dbl> NA, 9414, 9247, 8837, 8701, 8989, 9093, 8736, 9192, 9108, 9…
$ NH_AIAN_M <dbl> NA, 103, 87, 97, 94, 108, 128, 160, 178, 166, 205, 194, 179…
$ NH_AIAN_F <dbl> NA, 90, 93, 100, 115, 114, 130, 134, 162, 155, 193, 185, 20…
$ NH_API_M  <dbl> NA, 192, 146, 175, 150, 168, 170, 183, 171, 136, 177, 169, …
$ NH_API_F  <dbl> NA, 170, 182, 160, 157, 178, 158, 173, 177, 185, 179, 171, …
$ H_W_M     <dbl> NA, 223, 190, 198, 186, 190, 210, 188, 178, 182, 221, 194, …
$ H_W_F     <dbl> NA, 220, 196, 173, 191, 190, 170, 172, 179, 173, 166, 175, …
$ H_B_M     <dbl> NA, 47, 41, 32, 35, 36, 30, 28, 27, 29, 32, 31, 33, 34, 32,…
$ H_B_F     <dbl> NA, 45, 47, 41, 30, 26, 37, 23, 35, 31, 28, 38, 22, 39, 29,…
$ H_AIAN_M  <dbl> NA, 1, 2, 1, 9, 5, 8, 2, 4, 6, 6, 0, 1, 9, 6, 7, 5, 2, 2, 4…
$ H_AIAN_F  <dbl> NA, 8, 0, 2, 1, 4, 5, 3, 4, 4, 3, 4, 2, 2, 7, 0, 2, 2, 1, 6…
$ H_API_M   <dbl> NA, 5, 7, 2, 3, 5, 11, 2, 7, 12, 10, 7, 5, 6, 5, 6, 6, 2, 1…
$ H_API_F   <dbl> NA, 5, 5, 5, 3, 14, 6, 7, 6, 3, 11, 5, 5, 7, 8, 6, 6, 7, 3,…

Notice also that the first row is all NA values from white space in the orginal table for 1990, this is probably true for each year. We can check them dimensions of our table using the base dim() function. When we filter for rows where YEAR is NA, we indeed see 10 rows, which is what we would expect if we have a row like this for each of the years in the decade. We see the same if we try a different variable. Now we will test to see how large our tibble is if we drop rows with NA values using the drop_na() function of tidyr. We that indeed our dimensions only changed by ten, so there are not other rows with missing values that we might not expect. So now we will resign the dem_90_99 variable after removing these rows.

dim(dem_90_99)

[1] 43870    19

dem_90_99 %>%
  filter(is.na(YEAR))

# A tibble: 10 x 19
    YEAR STATEFP   Age NH_W_M NH_W_F NH_B_M NH_B_F NH_AIAN_M NH_AIAN_F NH_API_M
   <dbl> <chr>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 2    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 3    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 4    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 5    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 6    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 7    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 8    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 9    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
10    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
# … with 9 more variables: NH_API_F <dbl>, H_W_M <dbl>, H_W_F <dbl>,
#   H_B_M <dbl>, H_B_F <dbl>, H_AIAN_M <dbl>, H_AIAN_F <dbl>, H_API_M <dbl>,
#   H_API_F <dbl>

dem_90_99 %>%
  filter(is.na(Age))

# A tibble: 10 x 19
    YEAR STATEFP   Age NH_W_M NH_W_F NH_B_M NH_B_F NH_AIAN_M NH_AIAN_F NH_API_M
   <dbl> <chr>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 2    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 3    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 4    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 5    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 6    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 7    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 8    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 9    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
10    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
# … with 9 more variables: NH_API_F <dbl>, H_W_M <dbl>, H_W_F <dbl>,
#   H_B_M <dbl>, H_B_F <dbl>, H_AIAN_M <dbl>, H_AIAN_F <dbl>, H_API_M <dbl>,
#   H_API_F <dbl>

dem_90_99 %>%drop_na()

# A tibble: 43,860 x 19
    YEAR STATEFP   Age NH_W_M NH_W_F NH_B_M NH_B_F NH_AIAN_M NH_AIAN_F NH_API_M
   <dbl> <chr>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1  1990 01          0  20406  19101   9794   9414       103        90      192
 2  1990 01          1  19393  18114   9475   9247        87        93      146
 3  1990 01          2  18990  18043   9097   8837        97       100      175
 4  1990 01          3  19246  17786   9002   8701        94       115      150
 5  1990 01          4  19502  18366   9076   8989       108       114      168
 6  1990 01          5  19560  18386   9169   9093       128       130      170
 7  1990 01          6  19091  18047   8919   8736       160       134      183
 8  1990 01          7  19605  18316   9219   9192       178       162      171
 9  1990 01          8  18823  17743   9247   9108       166       155      136
10  1990 01          9  20226  19178  10194   9784       205       193      177
# … with 43,850 more rows, and 9 more variables: NH_API_F <dbl>, H_W_M <dbl>,
#   H_W_F <dbl>, H_B_M <dbl>, H_B_F <dbl>, H_AIAN_M <dbl>, H_AIAN_F <dbl>,
#   H_API_M <dbl>, H_API_F <dbl>

dem_90_99 %<>%drop_na()

Then we sum across the nonhispanic and hispaninc groups because this information is not available for the other previous decades. Then we will remove the variables for the hispanic and nonhispanic subgroups using select().

dem_90_99%<>%
    mutate(W_M = NH_W_M + H_W_M,
           W_F = NH_W_F + H_W_F,
           B_M = NH_B_M + H_B_M,
           B_F = NH_B_F + H_B_F,
           AIAN_M = NH_AIAN_M + H_AIAN_M,
           AIAN_F = NH_AIAN_F + H_AIAN_F,
           API_M = NH_API_M + H_API_M,
           API_F = NH_API_F + H_API_F) %>%
  select(-starts_with("NH_"), -starts_with("H_"))

glimpse(dem_90_99)

Rows: 43,860
Columns: 11
$ YEAR    <dbl> 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1…
$ STATEFP <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "…
$ Age     <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
$ W_M     <dbl> 20629, 19583, 19188, 19432, 19692, 19770, 19279, 19783, 19005…
$ W_F     <dbl> 19321, 18310, 18216, 17977, 18556, 18556, 18219, 18495, 17916…
$ B_M     <dbl> 9841, 9516, 9129, 9037, 9112, 9199, 8947, 9246, 9276, 10226, …
$ B_F     <dbl> 9459, 9294, 8878, 8731, 9015, 9130, 8759, 9227, 9139, 9812, 1…
$ AIAN_M  <dbl> 104, 89, 98, 103, 113, 136, 162, 182, 172, 211, 194, 180, 209…
$ AIAN_F  <dbl> 98, 93, 102, 116, 118, 135, 137, 166, 159, 196, 189, 204, 198…
$ API_M   <dbl> 197, 153, 177, 153, 173, 181, 185, 178, 148, 187, 176, 164, 1…
$ API_F   <dbl> 175, 187, 165, 160, 192, 164, 180, 183, 188, 190, 176, 168, 1…

Looking better! We also need to add age groups like the other decades. We will take a look at the 80s data using the distinct() function of the dplyr package to see what age groups we need. We can use the base cut() function to create a new variable with mutate() called AGE_GROUP that will have a label for every change in 5 years of age. The right = FALSE argument specifies that the interval is not closed on the right, meaning that if the value is at the cutpoint like the Age value is 5, then it will be in the 5 to 9 years group.

We can make the labels for the AGE_GROUP variable match those of dem_77_79 but we need to pull out the values of the tibble created by distinct(). To do this we can use the pull() function from the dplyr package. Note that it is important to check that the AGE_GROUP values are listed in order for dem_77_79. We will also remove the Age variable after we create the new AGE_GROUP variable for the dem_90_99 data.

distinct(dem_77_79, AGE_GROUP)

# A tibble: 18 x 1
   AGE_GROUP        
   <chr>            
 1 Under 5 years    
 2 5 to 9 years     
 3 10 to 14 years   
 4 15 to 19 years   
 5 20 to 24 years   
 6 25 to 29 years   
 7 30 to 34 years   
 8 35 to 39 years   
 9 40 to 44 years   
10 45 to 49 years   
11 50 to 54 years   
12 55 to 59 years   
13 60 to 64 years   
14 65 to 69 years   
15 70 to 74 years   
16 75 to 79 years   
17 80 to 84 years   
18 85 years and over

pull(distinct(dem_77_79, AGE_GROUP))

 [1] "Under 5 years"     "5 to 9 years"      "10 to 14 years"   
 [4] "15 to 19 years"    "20 to 24 years"    "25 to 29 years"   
 [7] "30 to 34 years"    "35 to 39 years"    "40 to 44 years"   
[10] "45 to 49 years"    "50 to 54 years"    "55 to 59 years"   
[13] "60 to 64 years"    "65 to 69 years"    "70 to 74 years"   
[16] "75 to 79 years"    "80 to 84 years"    "85 years and over"

dem_90_99 %<>%
  mutate(AGE_GROUP = cut(Age,
                         breaks = seq(0,90, by=5),
                         right = FALSE, labels = pull(distinct(dem_77_79,AGE_GROUP), AGE_GROUP))) %>%
  select(-Age)

glimpse(dem_90_99)

Rows: 43,860
Columns: 11
$ YEAR      <dbl> 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990,…
$ STATEFP   <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01",…
$ W_M       <dbl> 20629, 19583, 19188, 19432, 19692, 19770, 19279, 19783, 190…
$ W_F       <dbl> 19321, 18310, 18216, 17977, 18556, 18556, 18219, 18495, 179…
$ B_M       <dbl> 9841, 9516, 9129, 9037, 9112, 9199, 8947, 9246, 9276, 10226…
$ B_F       <dbl> 9459, 9294, 8878, 8731, 9015, 9130, 8759, 9227, 9139, 9812,…
$ AIAN_M    <dbl> 104, 89, 98, 103, 113, 136, 162, 182, 172, 211, 194, 180, 2…
$ AIAN_F    <dbl> 98, 93, 102, 116, 118, 135, 137, 166, 159, 196, 189, 204, 1…
$ API_M     <dbl> 197, 153, 177, 153, 173, 181, 185, 178, 148, 187, 176, 164,…
$ API_F     <dbl> 175, 187, 165, 160, 192, 164, 180, 183, 188, 190, 176, 168,…
$ AGE_GROUP <fct> Under 5 years, Under 5 years, Under 5 years, Under 5 years,…

Like the previous decades we will create a RACE and SUB_POP variable using pivot_longer() to create a single Race variable out of all the subgroup variables.

Now we need to collapse the data for the various races so that it matches the previous decades. This time we will use the case_when() function of the dplyr package and the str_detect() function of the stringr package to identify when the race is something other than B or W and replace with the value Other. The value to the right of the ~ indicates what we want the value of the new variable to be if the value of the variable we are using with str_decect() matches the condition specified. If the value does not match the specified condition, than the other values will be what ever is listed after TRUE ~. We will then create population counts as we did previously for the other decades.

Finally, we will create new sums for the subpopulations where we sum across the two Other subgroups Race to a create a single value for each value of YEAR, SEX, AGE_GROUP, and STATE by using the group_by() function and summarie().

dem_90_99  %<>%
  pivot_longer(cols = c(starts_with("W_"),
                    starts_with("B_"),
                    starts_with("AIAN_"),
                    starts_with("API_")),
               names_to = "RACE",
               values_to = "SUB_POP_temp")

dem_90_99 %<>%
  mutate(SEX = case_when(str_detect(RACE, "_M") ~ "Male",
                         TRUE ~ "Female"),
         RACE = case_when(str_detect(RACE, "W_") ~ "White",
                          str_detect(RACE, "B_") ~ "Black",
                          TRUE ~ "Other")) %>%
  left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

dem_90_99 %<>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups="drop")

pop_90_99 <- dem_90_99 %>%
  group_by(YEAR, STATE) %>%
  summarise(TOT_POP = sum(SUB_POP), .groups = "drop")

dem_90_99 <- dem_90_99 %>%
  left_join(pop_90_99, by=c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_90_99

# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP     SEX    RACE  PERC_SUB_POP
   <dbl> <chr>   <fct>         <chr>  <chr>        <dbl>
 1  1990 Alabama Under 5 years Female Black       1.12  
 2  1990 Alabama Under 5 years Female Other       0.0347
 3  1990 Alabama Under 5 years Female White       2.28  
 4  1990 Alabama Under 5 years Male   Black       1.15  
 5  1990 Alabama Under 5 years Male   Other       0.0336
 6  1990 Alabama Under 5 years Male   White       2.43  
 7  1990 Alabama 5 to 9 years  Female Black       1.14  
 8  1990 Alabama 5 to 9 years  Female Other       0.0419
 9  1990 Alabama 5 to 9 years  Female White       2.29  
10  1990 Alabama 5 to 9 years  Male   Black       1.16  
# … with 55,070 more rows

Again, we should check to make sure that we have the total values we would expect. We have the same number of unique values for each of our variables as in with the data from the 80s, so if we collpased the data for the different additional subpopulations in this data, then we have done it correctly.

Indeed it looks like we have 55,080 rows, which is what we would expect and is the same as the number of rows of the final dem_80_89 data. Looks good!

2000-2010

Again, for this decade we need to combine the data across years.

dem_00_10 <- dem_00_10 %>%
  map_df(bind_rows)

glimpse(dem_00_10)

Rows: 62,244
Columns: 21
$ REGION            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ DIVISION          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ STATE             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NAME              <chr> "United States", "United States", "United States", …
$ SEX               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ORIGIN            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ RACE              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ AGEGRP            <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
$ ESTIMATESBASE2000 <dbl> 281424600, 19176154, 20549855, 20528425, 20218782, …
$ POPESTIMATE2000   <dbl> 282162411, 19178293, 20463852, 20637696, 20294955, …
$ POPESTIMATE2001   <dbl> 284968955, 19298217, 20173362, 20978678, 20456284, …
$ POPESTIMATE2002   <dbl> 287625193, 19429192, 19872417, 21261421, 20610370, …
$ POPESTIMATE2003   <dbl> 290107933, 19592446, 19620851, 21415353, 20797166, …
$ POPESTIMATE2004   <dbl> 292805298, 19785885, 19454237, 21411680, 21102552, …
$ POPESTIMATE2005   <dbl> 295516599, 19917400, 19389067, 21212579, 21486214, …
$ POPESTIMATE2006   <dbl> 298379912, 19938883, 19544688, 21033138, 21807709, …
$ POPESTIMATE2007   <dbl> 301231207, 20125962, 19714611, 20841042, 22067816, …
$ POPESTIMATE2008   <dbl> 304093966, 20271127, 19929602, 20706655, 22210880, …
$ POPESTIMATE2009   <dbl> 306771529, 20244518, 20182499, 20660564, 22192810, …
$ CENSUS2010POP     <dbl> 308745538, 20201362, 20348657, 20677194, 22040343, …
$ POPESTIMATE2010   <dbl> 309349689, 20200529, 20382409, 20694011, 21959087, …

Ok, the data looks a bit different from the others. First we will remove a couple of variables that we probably don’t need. Also it looks like we have some values for the entire United Sates and we will drop these to be like the other decades.

dem_00_10 %<>%
  select(-ESTIMATESBASE2000,-CENSUS2010POP) %>%
  filter(NAME != "United States")

We can see that there are lots of values that are zero. According to the technical documentation for this data, zero values indicate the total for the other categories of Sex, Origin, Race, and AGEGRP.

So we will drop the total values for SEX, RACE, and AGEGRP by removing the rows where these variables are equal to zero.

We will also want to only select for the total values for Origin as we do not wish to divide the data into subgroups about hispanic ethnicity because we do not have that information for the first two decades. Thus we will filter for only the rows where Origin is equal to zero.

We will also then remove the REGION, Division, STATE, and Origin variables. We will then rename NAME to be STATE and rename AGEGRP to be like the other decades as AGE_GROUP.

dem_00_10 %<>%
  filter(SEX != 0,
         RACE != 0,
         AGEGRP != 0, 
         ORIGIN == 0) %>%
  dplyr::select(-REGION, -DIVISION, -ORIGIN, -STATE) %>%
  rename("STATE" = NAME,
         "AGE_GROUP" = AGEGRP)

dem_00_10

# A tibble: 11,016 x 15
   STATE   SEX  RACE AGE_GROUP POPESTIMATE2000 POPESTIMATE2001 POPESTIMATE2002
   <chr> <dbl> <dbl>     <dbl>           <dbl>           <dbl>           <dbl>
 1 Alab…     1     1         1           99527           99985           99578
 2 Alab…     1     1         2          104423          102518          101023
 3 Alab…     1     1         3          108325          108412          108059
 4 Alab…     1     1         4          108638          107370          107337
 5 Alab…     1     1         5          104337          107230          108195
 6 Alab…     1     1         6          106491          101466           98949
 7 Alab…     1     1         7          110116          110630          110416
 8 Alab…     1     1         8          123719          120283          116502
 9 Alab…     1     1         9          124961          125443          124751
10 Alab…     1     1        10          115024          117010          119354
# … with 11,006 more rows, and 8 more variables: POPESTIMATE2003 <dbl>,
#   POPESTIMATE2004 <dbl>, POPESTIMATE2005 <dbl>, POPESTIMATE2006 <dbl>,
#   POPESTIMATE2007 <dbl>, POPESTIMATE2008 <dbl>, POPESTIMATE2009 <dbl>,
#   POPESTIMATE2010 <dbl>

Now we need to recode the numeric values to the values in the techincal documentation. We can do so by adding labels to each numeric level using the base function factor().

dem_00_10 %<>%
  mutate(SEX = factor(SEX,
                            levels = 1:2,
                            labels = c("Male",
                                    "Female")),
         RACE = factor(RACE,
                            levels = 1:6,
                            labels = c("White",
                                    "Black",
                                    rep("Other",4))),
         AGE_GROUP = factor(AGE_GROUP,
                            levels = 1:18,
                            labels = pull(distinct(dem_77_79,AGE_GROUP), AGE_GROUP)))
                            
glimpse(dem_00_10)

Rows: 11,016
Columns: 15
$ STATE           <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama"…
$ SEX             <fct> Male, Male, Male, Male, Male, Male, Male, Male, Male,…
$ RACE            <fct> White, White, White, White, White, White, White, Whit…
$ AGE_GROUP       <fct> Under 5 years, 5 to 9 years, 10 to 14 years, 15 to 19…
$ POPESTIMATE2000 <dbl> 99527, 104423, 108325, 108638, 104337, 106491, 110116…
$ POPESTIMATE2001 <dbl> 99985, 102518, 108412, 107370, 107230, 101466, 110630…
$ POPESTIMATE2002 <dbl> 99578, 101023, 108059, 107337, 108195, 98949, 110416,…
$ POPESTIMATE2003 <dbl> 99627, 99920, 108026, 107749, 109360, 98276, 109893, …
$ POPESTIMATE2004 <dbl> 99788, 99306, 107627, 108666, 109037, 98742, 107653, …
$ POPESTIMATE2005 <dbl> 100316, 99754, 106570, 110278, 108727, 100327, 105151…
$ POPESTIMATE2006 <dbl> 100820, 101251, 106228, 111640, 108847, 103869, 10161…
$ POPESTIMATE2007 <dbl> 101766, 101985, 106243, 112353, 109496, 105175, 99917…
$ POPESTIMATE2008 <dbl> 102304, 102479, 106155, 113305, 110007, 106348, 99921…
$ POPESTIMATE2009 <dbl> 101411, 102688, 106130, 113741, 111167, 106497, 10138…
$ POPESTIMATE2010 <dbl> 99480, 102939, 106324, 112272, 112423, 106593, 102923…

OK, we also want to change the shape of the data so that we have a YEAR variable and each estimate of the population is a value in a new variable called SUB_POP_temp.

dem_00_10 %<>%
  pivot_longer(cols=contains("ESTIMATE"),
               names_to = "YEAR",
               values_to = "SUB_POP_temp")

We will now clean up the YEAR variable to only be the numeric value by keeping only the last 4 values of each string using the str_sub() function of the stringr package.

dem_00_10 %<>%
  mutate(YEAR = str_sub(YEAR, start=-4)) %>%
  mutate(YEAR = as.numeric(YEAR))

Now we will collapse the data for the different RACES and calculate a new SUB_POP value.

dem_00_10 %<>%
  group_by(YEAR, AGE_GROUP, STATE, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups = "drop")

Agian, the dimensions look as we expect with 60,588 rows. This time we have two levels of SEX, three levels of Race, 11 levels of YEAR, eighteen levels of AGE_GROUP, and fifty one levels of STATE. If we multiply this together we get 16,588. Looks good!

Now we will calculate the total polutation and percent of the total as we have done with the previous decades.

pop_00_10 <- dem_00_10 %>%
  group_by(YEAR, STATE) %>%
  summarise(TOT_POP = sum(SUB_POP), .groups = "drop")

We can also check that our wrangling was performecd correctly by summing the values for the individual subpopulations percentages and seeing if it totals to 100.

dem_00_10 %>%
  left_join(pop_00_10, by=c("YEAR", "STATE")) %>%
  group_by(YEAR, STATE) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  summarise(perc_tot = sum(PERC_SUB_POP), .groups = "drop") %>%
  mutate(poss_error = case_when(abs(perc_tot - 100) > 0 ~ TRUE,
                                TRUE ~ FALSE)) %>%
  group_by(poss_error) %>%
  tally()

# A tibble: 1 x 2
  poss_error     n
  <lgl>      <int>
1 FALSE        561

Looks like the percentages for each state for each year all add up to 100, as we would expect. Great! Now we will reasign the dem_00_10 data with this processing.

dem_00_10 %<>%
  left_join(pop_00_10, by = c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
 select(-SUB_POP, -TOT_POP)

dem_00_10

# A tibble: 60,588 x 6
    YEAR AGE_GROUP     STATE   SEX    RACE  PERC_SUB_POP
   <dbl> <fct>         <chr>   <fct>  <fct>        <dbl>
 1  2000 Under 5 years Alabama Male   White       2.24  
 2  2000 Under 5 years Alabama Male   Black       1.05  
 3  2000 Under 5 years Alabama Male   Other       0.101 
 4  2000 Under 5 years Alabama Female White       2.12  
 5  2000 Under 5 years Alabama Female Black       1.03  
 6  2000 Under 5 years Alabama Female Other       0.0995
 7  2000 Under 5 years Alaska  Male   White       2.35  
 8  2000 Under 5 years Alaska  Male   Black       0.165 
 9  2000 Under 5 years Alaska  Male   Other       1.37  
10  2000 Under 5 years Alaska  Female White       2.26  
# … with 60,578 more rows

OK, now we are ready to combine all of our demgraphic data together!

Combining demographic data

We can check that the colnames are the same for the data for each of the decades by using the setequal() function of the dplyr package.

setequal(colnames(dem_77_79),colnames(dem_80_89))

[1] TRUE

setequal(colnames(dem_80_89),colnames(dem_90_99))

[1] TRUE

setequal(colnames(dem_90_99),colnames(dem_00_10))

[1] TRUE

We can also confirm that we have the same number of age groups for each decade by using the base length() function. If you did not take a look at the wrangling for the demographic data then you may be unfamiliar with the pull() function of the dplyr package. This allows you to grab the values of a variable from a tibble. The distinct() function which is also of the dplyr package creates a tibble of the unique values for a variable.

length(pull(distinct(dem_77_79, AGE_GROUP), AGE_GROUP))

[1] 18

length(pull(distinct(dem_80_89, AGE_GROUP), AGE_GROUP))

[1] 18

length(pull(distinct(dem_90_99, AGE_GROUP), AGE_GROUP))

[1] 18

length(pull(distinct(dem_00_10, AGE_GROUP), AGE_GROUP))

[1] 18

Looks good!

Now we will combine the data using the bind_rows() function of the dplyr package. This function appends the data together based on the presence of columns with the same name in the different tibbles.

dem <- bind_rows(dem_77_79,
                 dem_80_89,
                 dem_90_99,
                 dem_00_10)

glimpse(dem)

Rows: 187,272
Columns: 6
$ YEAR         <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 19…
$ STATE        <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "…
$ SEX          <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male", …
$ RACE         <chr> "White", "White", "White", "White", "White", "White", "W…
$ AGE_GROUP    <chr> "Under 5 years", "5 to 9 years", "10 to 14 years", "15 t…
$ PERC_SUB_POP <dbl> 2.6123502, 2.9970356, 3.2545853, 3.5780690, 3.3324688, 2…

Great! now we have a really large single tibble.

Now we want to select similar demographic data to what was used in the previous analyses.

Here is the table from the Donohue paper that compares the data used in the analyses.

We can see that only the percentage of males that were from age 15-39 of the race groups (black, white, and other) were used in the Donohue analysis.

Ultimately we intend to make a tibble of data that is similar to each analysis. Therefore, we will create a data tibble about the demogrphaic data for each analysis now.

To do so we will first create a vector of the age groups that should be included in the Donohue-like analysis, that we will call DONOHUE_AGE_GROUPS. We will then filter for only the age groups in this vector by using the filter() function of the dplyr package and the %in% operator to indicate that we want to keep all AGE_GROUP values that are equal to those within DONOHUE_AGE_GROUPS. We also want to filter for only population percentages for males by using the == operator. Then we can collpase the age groups from 20-39 by using the fct_collpase() function of the forcats package.

DONOHUE_AGE_GROUPS <- c("15 to 19 years",
                        "20 to 24 years",
                        "25 to 29 years",
                        "30 to 34 years",
                        "35 to 39 years")

dem_DONOHUE <- dem %>%
  filter(AGE_GROUP %in% DONOHUE_AGE_GROUPS,
               SEX == "Male") %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP, "20 to 39 years"=c("20 to 24 years",
                                                                "25 to 29 years",
                                                                "30 to 34 years",
                                                                "35 to 39 years")))

dem_DONOHUE

# A tibble: 26,010 x 6
    YEAR STATE   SEX   RACE  AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <fct>                 <dbl>
 1  1977 Alabama Male  White 15 to 19 years        3.58 
 2  1977 Alabama Male  White 20 to 39 years        3.33 
 3  1977 Alabama Male  White 20 to 39 years        2.95 
 4  1977 Alabama Male  White 20 to 39 years        2.66 
 5  1977 Alabama Male  White 20 to 39 years        2.14 
 6  1977 Alabama Male  Black 15 to 19 years        1.55 
 7  1977 Alabama Male  Black 20 to 39 years        1.16 
 8  1977 Alabama Male  Black 20 to 39 years        0.820
 9  1977 Alabama Male  Black 20 to 39 years        0.596
10  1977 Alabama Male  Black 20 to 39 years        0.462
# … with 26,000 more rows

We also want to create a new variable that will contain all the demographic information for each percentage just as was done in the Donohue, et al. analysis. This should result in 6 different demographic variables.

To do this we will modify the AGE_GROUP variable by using the mutate() function of the dplyr package. We will replace the spaces in the now two age group categorise with and undesrscore using the str_replace_all() function of the stringr package which replaces all instances of a pattern in a character string.

Then we will use the group_by() function and the summarise() funtion also of the dplyr package to allow us to calculate a sum of the percentages for each of the subpopulation percentages for the newly modifed age groups in AGE_GROUP. The .groups = "drop" argument allows for the grouping to be removed after the summarise() function.

dem_DONOHUE %<>%
  mutate(AGE_GROUP = str_replace_all(string = AGE_GROUP, 
                                     pattern = " ", 
                                     replacement = "_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop")

dem_DONOHUE

# A tibble: 10,404 x 6
    YEAR STATE   RACE  SEX   AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <chr>                 <dbl>
 1  1977 Alabama Black Male  15_to_19_years       1.55  
 2  1977 Alabama Black Male  20_to_39_years       3.04  
 3  1977 Alabama Other Male  15_to_19_years       0.0178
 4  1977 Alabama Other Male  20_to_39_years       0.0642
 5  1977 Alabama White Male  15_to_19_years       3.58  
 6  1977 Alabama White Male  20_to_39_years      11.1   
 7  1977 Alaska  Black Male  15_to_19_years       0.163 
 8  1977 Alaska  Black Male  20_to_39_years       0.968 
 9  1977 Alaska  Other Male  15_to_19_years       1.12  
10  1977 Alaska  Other Male  20_to_39_years       2.73  
# … with 10,394 more rows

Now we will combine the variables RACE, SEX, and AGE_GROUP together into one string separated by underscores using the unite function of the tidyr package. we will call this new variable VARIABLE. We will rename the PERC_SUB_POP variable to be VALUE using the rename() function of the dplyr package. The new name should be listed first before the =.

dem_DONOHUE %<>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE" = PERC_SUB_POP)

dem_DONOHUE

# A tibble: 10,404 x 4
    YEAR STATE   VARIABLE                    VALUE
   <dbl> <chr>   <chr>                       <dbl>
 1  1977 Alabama Black_Male_15_to_19_years  1.55  
 2  1977 Alabama Black_Male_20_to_39_years  3.04  
 3  1977 Alabama Other_Male_15_to_19_years  0.0178
 4  1977 Alabama Other_Male_20_to_39_years  0.0642
 5  1977 Alabama White_Male_15_to_19_years  3.58  
 6  1977 Alabama White_Male_20_to_39_years 11.1   
 7  1977 Alaska  Black_Male_15_to_19_years  0.163 
 8  1977 Alaska  Black_Male_20_to_39_years  0.968 
 9  1977 Alaska  Other_Male_15_to_19_years  1.12  
10  1977 Alaska  Other_Male_20_to_39_years  2.73  
# … with 10,394 more rows

Let’s do a quick row number check. We have six different demographic variables, 51 states (DC counts as a state in this case), and 34 different years from 1977 to 2010, we should have 10,404 rows, which we do!

Now, let’s do the same for the “Lott-like” analysis.

So, in this analysis there were 36 variables covering percentages of indiviuals from 10 to over 65, three race groups and both males and females. This table is misprinted and does not include the word “Other” for the third race group that was used.

First we will filter out the age groups that were not included. Then we will collapse the age groups to those that were used by Lott et al. again using the fct_collpase() function of the forcats package.

Also we will again combine the values across the variables to create a new demographic varaible with 36 levels.

LOTT_AGE_GROUPS_NULL <- c("Under 5 years",
                          "5 to 9 years")

dem_LOTT <- dem %>%
  filter(!(AGE_GROUP %in% LOTT_AGE_GROUPS_NULL) )%>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP,
                                  "10 to 19 years"=c("10 to 14 years",
                                                     "15 to 19 years"),
                                  "20 to 29 years"=c("20 to 24 years",
                                                     "25 to 29 years"),
                                  "30 to 39 years"=c("30 to 34 years",
                                                     "35 to 39 years"),
                                  "40 to 49 years"=c("40 to 44 years",
                                                     "45 to 49 years"),
                                  "50 to 64 years"=c("50 to 54 years",
                                                     "55 to 59 years",
                                                     "60 to 64 years"),
                                  "65 years and over"=c("65 to 69 years",
                                                        "70 to 74 years",
                                                        "75 to 79 years",
                                                        "80 to 84 years",
                                                        "85 years and over"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)

We can indeed check that we have the correct number of levels for VARIABLE using the distinct() function.

 distinct(dem_LOTT, VARIABLE)

# A tibble: 36 x 1
   VARIABLE                      
   <chr>                         
 1 Black_Female_10_to_19_years   
 2 Black_Female_20_to_29_years   
 3 Black_Female_30_to_39_years   
 4 Black_Female_40_to_49_years   
 5 Black_Female_50_to_64_years   
 6 Black_Female_65_years_and_over
 7 Black_Male_10_to_19_years     
 8 Black_Male_20_to_29_years     
 9 Black_Male_30_to_39_years     
10 Black_Male_40_to_49_years     
# … with 26 more rows

Combining population Data

We also have population data for each decade that came from wrangling the demogrphic data.

We again want to combine this data, so let’s again make sure that all the different tibbles have the same column names.

setequal(colnames(pop_77_79),colnames(pop_80_89))

[1] TRUE

setequal(colnames(pop_80_89),colnames(pop_90_99))

[1] TRUE

setequal(colnames(pop_90_99),colnames(pop_00_10))

[1] TRUE

head(pop_77_79)

# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1977 Alabama     3782571
2  1977 Alaska       397220
3  1977 Arizona     2427296
4  1977 Arkansas    2207195
5  1977 California 22350332
6  1977 Colorado    2696179

head(pop_80_89)

# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1980 Alabama     3899671
2  1980 Alaska       404680
3  1980 Arizona     2735840
4  1980 Arkansas    2288809
5  1980 California 23792840
6  1980 Colorado    2909545

head(pop_90_99)

# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1990 Alabama     4048508
2  1990 Alaska       553120
3  1990 Arizona     3679056
4  1990 Arkansas    2354343
5  1990 California 29950111
6  1990 Colorado    3303862

head(pop_00_10)

# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  2000 Alabama     4452173
2  2000 Alaska       627963
3  2000 Arizona     5160586
4  2000 Arkansas    2678588
5  2000 California 33987977
6  2000 Colorado    4326921

Looks good!

population_data <- bind_rows(pop_77_79,
                             pop_80_89,
                             pop_90_99,
                             pop_00_10)

population_data <- population_data %>%
  mutate(VARIABLE = "Population") %>%
  rename("VALUE" = TOT_POP)

We could check that we have 51 values for each year by using the count() function of the dplyr package.

population_data %>%
  count(YEAR)

# A tibble: 34 x 2
    YEAR     n
   <dbl> <int>
 1  1977    51
 2  1978    51
 3  1979    51
 4  1980    51
 5  1981    51
 6  1982    51
 7  1983    51
 8  1984    51
 9  1985    51
10  1986    51
# … with 24 more rows

Police staffing

Click here to see details about how the plice staffing data was wrangled.

OK, now we will wrangle the police staffing data. We want to limit the data to only the years of interest. Then we will also replace NA values with zero for the male_total_ct and female_total_ct variables using the replace_na() function of the tidyr packge. We will also, use the across() function of the dplyr package to select and mutate both of these columns in this way. Since both of these variables have total_ct in the name and no other variables do, we can use the contains() function of the dplyr package to specify that we want to use these columns instead of listing both out.

avocado… why not 2010….

glimpse(ps_data)

Rows: 1,439,467
Columns: 21
$ data_year             <dbl> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960,…
$ ori                   <chr> "AK020045Y", "AL0011000", "AL0160600", "AL01900…
$ pub_agency_name       <chr> "Alcohol Beverage Control Board", "Homewood", "…
$ pub_agency_unit       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ state_abbr            <chr> "AK", "AL", "AL", "AL", "AL", "AL", "AL", "AL",…
$ division_name         <chr> "Pacific", "East South Central", "East South Ce…
$ region_name           <chr> "West", "South", "South", "South", "South", "So…
$ county_name           <chr> "N/A", "JEFFERSON", "N/A", "COFFEE", "N/A", "HA…
$ agency_type_name      <chr> "Other State Agency", "City", "City", "County",…
$ population_group_desc <chr> "Cities under 2,500", "Cities from 10,000 thru …
$ population            <dbl> 0, 20289, 0, 14852, 0, 3081, 0, 18739, 2776, 0,…
$ male_officer_ct       <dbl> NA, 17, NA, 0, NA, 0, NA, 0, 0, NA, NA, 0, NA, …
$ male_civilian_ct      <dbl> NA, 3, NA, 0, NA, 0, NA, 0, 0, NA, NA, 0, NA, N…
$ male_total_ct         <dbl> NA, 20, NA, 0, NA, 0, NA, 0, 0, NA, NA, 0, NA, …
$ female_officer_ct     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ female_civilian_ct    <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ female_total_ct       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ officer_ct            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ civilian_ct           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ total_pe_ct           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ pe_ct_per_1000        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

ps_data %<>%
  filter(data_year >= 1977, 
         data_year <= 2014) %>%
mutate(across(.cols =contains("total_ct"), ~replace_na(., 0)))

glimpse(ps_data)

Rows: 932,063
Columns: 21
$ data_year             <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977,…
$ ori                   <chr> "AK0012000", "AK0012300", "AKASP0000", "AL00125…
$ pub_agency_name       <chr> "Soldotna", "Kenai", "Alaska State Troopers", "…
$ pub_agency_unit       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ state_abbr            <chr> "AK", "AK", "AK", "AL", "AL", "AL", "AL", "AL",…
$ division_name         <chr> "Pacific", "Pacific", "Pacific", "East South Ce…
$ region_name           <chr> "West", "West", "West", "South", "South", "Sout…
$ county_name           <chr> "KENAI PENINSULA", "KENAI PENINSULA", "N/A", "J…
$ agency_type_name      <chr> "City", "City", "State Police", "City", "Other"…
$ population_group_desc <chr> "Cities under 2,500", "Cities from 2,500 thru 9…
$ population            <dbl> 2131, 5800, 172397, 1000, 0, 8611, 2850, 975, 2…
$ male_officer_ct       <dbl> 6, 10, 0, 3, NA, 16, 6, 2, 5, NA, NA, 3, 26, 5,…
$ male_civilian_ct      <dbl> 6, 0, 0, 0, NA, 5, 0, 0, 0, NA, NA, 0, 0, 0, 4,…
$ male_total_ct         <dbl> 12, 10, 0, 3, 0, 21, 6, 2, 5, 0, 0, 3, 26, 5, 6…
$ female_officer_ct     <lgl> FALSE, FALSE, NA, FALSE, NA, FALSE, FALSE, FALS…
$ female_civilian_ct    <lgl> TRUE, NA, NA, FALSE, NA, FALSE, FALSE, FALSE, F…
$ female_total_ct       <dbl> 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 13, 0…
$ officer_ct            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ civilian_ct           <lgl> NA, NA, NA, FALSE, NA, NA, FALSE, FALSE, FALSE,…
$ total_pe_ct           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ pe_ct_per_1000        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Now we can create a new variable called officer_total which will be the sum of these variables. We will then keep just this variable as well as the data_year, pub_agency_name, and state_abbr.

ps_data %<>%
  mutate(officer_total = male_total_ct + female_total_ct) %>%
  dplyr::select(data_year,
                pub_agency_name,
                state_abbr,
                officer_total)

ps_data

# A tibble: 932,063 x 4
   data_year pub_agency_name                            state_abbr officer_total
       <dbl> <chr>                                      <chr>              <dbl>
 1      1977 Soldotna                                   AK                    13
 2      1977 Kenai                                      AK                    15
 3      1977 Alaska State Troopers                      AK                     0
 4      1977 Trafford                                   AL                     3
 5      1977 Trussville Fire Department Fire and Explo… AL                     0
 6      1977 Atmore                                     AL                    21
 7      1977 East Brewton                               AL                     6
 8      1977 Brilliant                                  AL                     2
 9      1977 Camden                                     AL                     5
10      1977 Drug Enforcement Administration, Birmingh… AL                     0
# … with 932,053 more rows

Now we also want to get collapse by pub_agency_name to get a total count for each year and each state. So we will do this by using the group_by() function and grouping by data_year and state_abbr and using the summarise() function to calculate a sum.

ps_data %<>%
  group_by(data_year, state_abbr) %>%
  summarise(officer_state_total=sum(officer_total), .groups = "drop")

ps_data

# A tibble: 2,242 x 3
   data_year state_abbr officer_state_total
       <dbl> <chr>                    <dbl>
 1      1977 AK                         544
 2      1977 AL                        7380
 3      1977 AR                        3344
 4      1977 AS                           0
 5      1977 AZ                        6414
 6      1977 CA                       65596
 7      1977 CO                        7337
 8      1977 CT                        6051
 9      1977 CZ                           0
10      1977 DC                        4751
# … with 2,232 more rows

And we will check that we have same number of values (the number of years included in the data) for each state.

ps_data %>%
  count(state_abbr)  %>% head()

# A tibble: 6 x 2
  state_abbr     n
  <chr>      <int>
1 AK            38
2 AL            38
3 AR            38
4 AS            38
5 AZ            38
6 CA            38

ps_data %>%
  count(state_abbr) %>%
  filter(n != 38) %>%
  dim()

[1] 0 2

Looks like all the states have 38 values.

Notice also that there are some unusual abrreviations in the state_abbr variable.

We will remove data for US terroitories and associated states

Abbreviation	Territory and associated states
AS	American Samoa
GM	Guam
CZ	Canal Zone
FS	??Federated States of Micronesia (usually FM)
MP	Northern Mariana Islands
OT	??U.S. Minor Outlying Islands (usually UM)
PR	Puerto Rico
VI	Virgin Islands

state_of_interest_NULL <- c("AS",
                            "GM",
                            "CZ",
                            "FS",
                            "MP",
                            "OT",
                            "PR",
                            "VI")

ps_data <- ps_data %>%
  filter(!(state_abbr %in% state_of_interest_NULL))

Within the datasets package that is loaded with R, there is a dataset called state that contains an object called state.abb that has the state abbreviations and state.name that has the state names. We will combine these now to add the state names to our data.

state_abb_data <- tibble( "state_abbr" = state.abb, "STATE" =state.name)
head(state_abb_data)

# A tibble: 6 x 2
  state_abbr STATE     
  <chr>      <chr>     
1 AL         Alabama   
2 AK         Alaska    
3 AZ         Arizona   
4 AR         Arkansas  
5 CA         California
6 CO         Colorado

One unusual thing about this data is that NE is used for Nebraska to avoid confusions with NB in Canada. So we want to repace that using the str_replace() function of the stringr package

state_abb_data %<>%
  mutate(state_abbr = str_replace(string = state_abbr, 
                                pattern = "NE", 
                            replacement = "NB"))

# Scroll through the output!
state_abb_data %>% print(n = 50)

# A tibble: 50 x 2
   state_abbr STATE         
   <chr>      <chr>         
 1 AL         Alabama       
 2 AK         Alaska        
 3 AZ         Arizona       
 4 AR         Arkansas      
 5 CA         California    
 6 CO         Colorado      
 7 CT         Connecticut   
 8 DE         Delaware      
 9 FL         Florida       
10 GA         Georgia       
11 HI         Hawaii        
12 ID         Idaho         
13 IL         Illinois      
14 IN         Indiana       
15 IA         Iowa          
16 KS         Kansas        
17 KY         Kentucky      
18 LA         Louisiana     
19 ME         Maine         
20 MD         Maryland      
21 MA         Massachusetts 
22 MI         Michigan      
23 MN         Minnesota     
24 MS         Mississippi   
25 MO         Missouri      
26 MT         Montana       
27 NB         Nebraska      
28 NV         Nevada        
29 NH         New Hampshire 
30 NJ         New Jersey    
31 NM         New Mexico    
32 NY         New York      
33 NC         North Carolina
34 ND         North Dakota  
35 OH         Ohio          
36 OK         Oklahoma      
37 OR         Oregon        
38 PA         Pennsylvania  
39 RI         Rhode Island  
40 SC         South Carolina
41 SD         South Dakota  
42 TN         Tennessee     
43 TX         Texas         
44 UT         Utah          
45 VT         Vermont       
46 VA         Virginia      
47 WA         Washington    
48 WV         West Virginia 
49 WI         Wisconsin     
50 WY         Wyoming

We need to add DC to this. We will use the add_row() function of dplyr to do this. We just need to specify values for both of the variables.

state_abb_data %<>%
  dplyr::add_row(state_abbr = "DC",
                      STATE = "District of Columbia")

Now we will add this to our police staffing data and then remove the state_abbr variable, so that we just have state names. We will also

ps_data %<>%
  left_join(state_abb_data, by = "state_abbr") %>%
  dplyr::select(-state_abbr)
ps_data

# A tibble: 1,938 x 3
   data_year officer_state_total STATE               
       <dbl>               <dbl> <chr>               
 1      1977                 544 Alaska              
 2      1977                7380 Alabama             
 3      1977                3344 Arkansas            
 4      1977                6414 Arizona             
 5      1977               65596 California          
 6      1977                7337 Colorado            
 7      1977                6051 Connecticut         
 8      1977                4751 District of Columbia
 9      1977                1018 Delaware            
10      1977               24588 Florida             
# … with 1,928 more rows

Now we will rename the variables to match those of the other datasets.

ps_data %<>%
  rename(YEAR = "data_year",
         VALUE = "officer_state_total") %>%
  mutate(VARIABLE = "officer_state_total")
ps_data

# A tibble: 1,938 x 4
    YEAR VALUE STATE                VARIABLE           
   <dbl> <dbl> <chr>                <chr>              
 1  1977   544 Alaska               officer_state_total
 2  1977  7380 Alabama              officer_state_total
 3  1977  3344 Arkansas             officer_state_total
 4  1977  6414 Arizona              officer_state_total
 5  1977 65596 California           officer_state_total
 6  1977  7337 Colorado             officer_state_total
 7  1977  6051 Connecticut          officer_state_total
 8  1977  4751 District of Columbia officer_state_total
 9  1977  1018 Delaware             officer_state_total
10  1977 24588 Florida              officer_state_total
# … with 1,928 more rows

We also need to adjust the value to be that of every 100,000 people in the state. To do so we need the population for each state, which lukily we already have. We will slightly modify the population data and create a new tibble that will make it more clear how we are dividing by it.

denominator_temp <- population_data %>%
 select(-VARIABLE) %>%
  rename("Population_temp"=VALUE)
head(denominator_temp)

# A tibble: 6 x 3
   YEAR STATE      Population_temp
  <dbl> <chr>                <dbl>
1  1977 Alabama            3782571
2  1977 Alaska              397220
3  1977 Arizona            2427296
4  1977 Arkansas           2207195
5  1977 California        22350332
6  1977 Colorado           2696179

ps_data %<>%
  left_join(denominator_temp, by=c("STATE","YEAR"))
head(ps_data)

# A tibble: 6 x 5
   YEAR VALUE STATE      VARIABLE            Population_temp
  <dbl> <dbl> <chr>      <chr>                         <dbl>
1  1977   544 Alaska     officer_state_total          397220
2  1977  7380 Alabama    officer_state_total         3782571
3  1977  3344 Arkansas   officer_state_total         2207195
4  1977  6414 Arizona    officer_state_total         2427296
5  1977 65596 California officer_state_total        22350332
6  1977  7337 Colorado   officer_state_total         2696179

Avocado not sure why the lag?- could use inside plm function…- conceptually I get why you might want to know how staffing has changed recently… but this does not seem to calculate that…hmmmm

ps_data %<>%
  mutate(VALUE = (VALUE * 100000) / Population_temp) %>%
  #mutate(VALUE = lag(VALUE)) %>%
  mutate(VARIABLE = "police_per_100k_lag") %>%
  select(-Population_temp)

ps_data

# A tibble: 1,938 x 4
    YEAR VALUE STATE                VARIABLE           
   <dbl> <dbl> <chr>                <chr>              
 1  1977  137. Alaska               police_per_100k_lag
 2  1977  195. Alabama              police_per_100k_lag
 3  1977  152. Arkansas             police_per_100k_lag
 4  1977  264. Arizona              police_per_100k_lag
 5  1977  293. California           police_per_100k_lag
 6  1977  272. Colorado             police_per_100k_lag
 7  1977  196. Connecticut          police_per_100k_lag
 8  1977  697. District of Columbia police_per_100k_lag
 9  1977  171. Delaware             police_per_100k_lag
10  1977  277. Florida              police_per_100k_lag
# … with 1,928 more rows

Unemployment

The first thing we need to do with the unemployment data is combine the data across the different states. We can do that using the bind_rows() function of dplyr which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the map_df() function of the purrr package to allow us to do this across each tibble in our list. We will then select just the annual data for each state and year and we will rename our variables to be consistent with some of other data that we are working with. Thus we would like our variables to be YEAR, VALUE and VARIABLE in all caps.

ue_rate_data <- ue_rate_data %>%
  map_df(bind_rows, .id = "STATE")

head(ue_rate_data)

# A tibble: 6 x 15
  STATE   Year   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov
  <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alaba…  1977   7.5   9     7.7   7.2   6.8   8.6   8     7.8   6.7   6.3   6.3
2 Alaba…  1978   7.1   6.9   6.2   5.4   5.1   6.9   6.7   6.7   6.5   6.3   6.3
3 Alaba…  1979   6.7   7.5   6.9   6.6   6.4   8.4   7.7   7.8   7.1   7.2   6.9
4 Alaba…  1980   7.7   7.8   7.4   7.4   8.4   9.7  10.4  10.3   9.3   9.6   9.4
5 Alaba…  1981  10    10.3   9.5   9.1   9.4  11.1  10.4  10.9  10.8  11.7  11.5
6 Alaba…  1982  13.2  13.2  12.9  12.6  12.8  14.5  14.7  14.8  14.7  15.1  15.4
# … with 2 more variables: Dec <dbl>, Annual <dbl>

ue_rate_data <- ue_rate_data %>%
  dplyr::select(STATE, Year, Annual) %>%
  rename("YEAR" = Year,
        "VALUE" = Annual) %>%
  mutate(VARIABLE = "Unemployment_rate")

head(ue_rate_data)

# A tibble: 6 x 4
  STATE    YEAR VALUE VARIABLE         
  <chr>   <dbl> <dbl> <chr>            
1 Alabama  1977   7.3 Unemployment_rate
2 Alabama  1978   6.4 Unemployment_rate
3 Alabama  1979   7.2 Unemployment_rate
4 Alabama  1980   8.9 Unemployment_rate
5 Alabama  1981  10.6 Unemployment_rate
6 Alabama  1982  14.1 Unemployment_rate

Poverty rate

Click here to see details about how the poverty data was wrangled

OK, now for wrangling the poverty data. First let’s take a look at it.

head(poverty_rate_data)

# A tibble: 6 x 6
  `NOTE: Number in thousa… ...2  ...3   ...4         ...5         ...6          
  <chr>                    <chr> <chr>  <chr>        <chr>        <chr>         
1 2018                     <NA>  <NA>    <NA>        <NA>          <NA>         
2 STATE                    Total Number "Standard\n… Percent      "Standard\ner…
3 Alabama                  4877  779    "65"         16           "1.3"         
4 Alaska                   720   94     "9"          13.1         "1.2"         
5 Arizona                  7241  929    "80"         12.80000000… "1.1000000000…
6 Arkansas                 2912  462    "38"         15.9         "1.3"

We can see that the column names are actually shifted downbelow the row with the year. So we will manually make these values the actual column names.

colnames(poverty_rate_data) <- c("STATE",
                                 "Total",
                                 "Number",
                                 "Number_se",
                                 "Percent",
                                 "Percent_se")

poverty_rate_data2 <-poverty_rate_data

Let’s also remove the rows where the column names are listed, like row number 2.

poverty_rate_data  %<>%
  filter(STATE != "STATE")
head(poverty_rate_data)

# A tibble: 6 x 6
  STATE      Total Number Number_se Percent            Percent_se        
  <chr>      <chr> <chr>  <chr>     <chr>              <chr>             
1 2018       <NA>  <NA>   <NA>      <NA>               <NA>              
2 Alabama    4877  779    65        16                 1.3               
3 Alaska     720   94     9         13.1               1.2               
4 Arizona    7241  929    80        12.800000000000001 1.1000000000000001
5 Arkansas   2912  462    38        15.9               1.3               
6 California 39150 4664   184       11.9               0.5

We can also see that there are some extra notes at the end of our data. This is why it is a good idea to look at both the head and tail of your data.

tail(poverty_rate_data)

# A tibble: 6 x 6
  STATE                             Total Number Number_se Percent Percent_se   
  <chr>                             <chr> <chr>  <chr>     <chr>   <chr>        
1 Wisconsin                         4724  403    57        8.5     1.1000000000…
2 Wyoming                           468   49     20        10.4    4            
3 Standard errors shown in this ta… <NA>  <NA>   <NA>      <NA>    <NA>         
4 For information on confidentiali… <NA>  <NA>   <NA>      <NA>    <NA>         
5 Footnotes are available at <www.… <NA>  <NA>   <NA>      <NA>    <NA>         
6 SOURCE: U.S. Bureau of the Censu… <NA>  <NA>   <NA>      <NA>    <NA>

We can see that the strings for the state for these rows are very long. We can also see that there are rows that just have the year, where the state is only 4 characters long. We will create a new variable called length_state based on the number of characters in the STATE values. We will use the str_length() function of the stringr package. We need to use the map_dbl() function to apply this to each row of the STATE variable. The map() function creates a list, whereas the map_dbl() function creates a vector of class double. If we were to use map() we would need to use unlist() and pull().

poverty_rate_data %<>%
 mutate(length_state = map_dbl(STATE, str_length))

# Alternatively with map()
#poverty_rate_data %<>%
#mutate(length_state = unlist(map(pull(poverty_rate_data, STATE), str_length)))

poverty_rate_data

# A tibble: 2,136 x 7
   STATE         Total Number Number_se Percent      Percent_se     length_state
   <chr>         <chr> <chr>  <chr>     <chr>        <chr>                 <dbl>
 1 2018          <NA>  <NA>   <NA>      <NA>         <NA>                      4
 2 Alabama       4877  779    65        16           1.3                       7
 3 Alaska        720   94     9         13.1         1.2                       6
 4 Arizona       7241  929    80        12.80000000… 1.10000000000…            7
 5 Arkansas      2912  462    38        15.9         1.3                       8
 6 California    39150 4664   184       11.9         0.5                      10
 7 Colorado      5739  521    51        9.099999999… 0.90000000000…            8
 8 Connecticut   3413  348    43        10.19999999… 1.3                      11
 9 Delaware      976   72     9         7.400000000… 1                         8
10 District of … 692   102    8         14.69999999… 1.10000000000…           20
# … with 2,126 more rows

Great, now let’s look at the tail with our new variable length_state

tail(pull(poverty_rate_data, length_state))

[1]   9   7 285 164 129 101

poverty_rate_data %<>% 
  filter(length_state <100)

tail(poverty_rate_data)

# A tibble: 6 x 7
  STATE       Total Number Number_se Percent        Percent_se      length_state
  <chr>       <chr> <chr>  <chr>     <chr>          <chr>                  <dbl>
1 Vermont     520   62     22        12             4                          7
2 Virginia    5204  647    72        12.4           1.3                        8
3 Washington  4223  538    65        12.6999999999… 1.399999999999…           10
4 West Virgi… 1952  297    49        15.1999999999… 2.299999999999…           13
5 Wisconsin   4724  403    57        8.5            1.100000000000…            9
6 Wyoming     468   49     20        10.4           4                          7

Looks good!

Now let’s select all the states that are actually year values to create a new variable about the year. We can do so by using the str_detect() function of the stringr package to look for digits or values of 0-9. This is indicated by using the "[:digit:]".

As you can see in the RStudio cheatsheet about regular expressions this notation indicates any digit between 0 and 9.

knitr::include_graphics(here("img", "regex.png"))

# Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  print(n = 51)

# A tibble: 41 x 7
   STATE     Total Number Number_se Percent Percent_se length_state
   <chr>     <chr> <chr>  <chr>     <chr>   <chr>             <dbl>
 1 2018      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 2 2017 (21) <NA>  <NA>   <NA>      <NA>    <NA>                  9
 3 2017      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 4 2016      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 5 2015      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 6 2014      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 7 2013 (19) <NA>  <NA>   <NA>      <NA>    <NA>                  9
 8 2013 (18) <NA>  <NA>   <NA>      <NA>    <NA>                  9
 9 2012      <NA>  <NA>   <NA>      <NA>    <NA>                  4
10 2011      <NA>  <NA>   <NA>      <NA>    <NA>                  4
11 2010 (17) <NA>  <NA>   <NA>      <NA>    <NA>                  9
12 2009      <NA>  <NA>   <NA>      <NA>    <NA>                  4
13 2008      <NA>  <NA>   <NA>      <NA>    <NA>                  4
14 2007      <NA>  <NA>   <NA>      <NA>    <NA>                  4
15 2006      <NA>  <NA>   <NA>      <NA>    <NA>                  4
16 2005      <NA>  <NA>   <NA>      <NA>    <NA>                  4
17 2004 (14) <NA>  <NA>   <NA>      <NA>    <NA>                  9
18 2003      <NA>  <NA>   <NA>      <NA>    <NA>                  4
19 2002      <NA>  <NA>   <NA>      <NA>    <NA>                  4
20 2001      <NA>  <NA>   <NA>      <NA>    <NA>                  4
21 2000 (12) <NA>  <NA>   <NA>      <NA>    <NA>                  9
22 1999 (11) <NA>  <NA>   <NA>      <NA>    <NA>                  9
23 1998      <NA>  <NA>   <NA>      <NA>    <NA>                  4
24 1997      <NA>  <NA>   <NA>      <NA>    <NA>                  4
25 1996      <NA>  <NA>   <NA>      <NA>    <NA>                  4
26 1995      <NA>  <NA>   <NA>      <NA>    <NA>                  4
27 1994      <NA>  <NA>   <NA>      <NA>    <NA>                  4
28 1993 (10) <NA>  <NA>   <NA>      <NA>    <NA>                  9
29 1992 (9)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
30 1991 (8)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
31 1990      <NA>  <NA>   <NA>      <NA>    <NA>                  4
32 1989      <NA>  <NA>   <NA>      <NA>    <NA>                  4
33 1988      <NA>  <NA>   <NA>      <NA>    <NA>                  4
34 1987 (7)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
35 1986      <NA>  <NA>   <NA>      <NA>    <NA>                  4
36 1985      <NA>  <NA>   <NA>      <NA>    <NA>                  4
37 1984      <NA>  <NA>   <NA>      <NA>    <NA>                  4
38 1983 (6)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
39 1982      <NA>  <NA>   <NA>      <NA>    <NA>                  4
40 1981 (5)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
41 1980      <NA>  <NA>   <NA>      <NA>    <NA>                  4

Some of the years (2013 and 2017) are listed twice with a number in parantheses, others are just listed once with a number in parantheses. Looking at the technical documentation, this seems to do with updates to the defition of poverty and to the methods used to estimate poverty levels. See here and here for more information. We will simply select one of the sets of data for 2013 and 2017.

poverty_rate_data %>% 
  filter(str_detect(STATE, "2013")) %>%
  filter(str_detect(STATE, "2017"))

# A tibble: 0 x 7
# … with 7 variables: STATE <chr>, Total <chr>, Number <chr>, Number_se <chr>,
#   Percent <chr>, Percent_se <chr>, length_state <dbl>

First let’s add the year value to our data.

There should be consistently data for 51 states (including DC). We can see that sometimes DC is spelled out and sometimes it is not.

### Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  distinct(STATE) %>% print(n = 100)

# A tibble: 52 x 1
   STATE               
   <chr>               
 1 Alabama             
 2 Alaska              
 3 Arizona             
 4 Arkansas            
 5 California          
 6 Colorado            
 7 Connecticut         
 8 Delaware            
 9 District of Columbia
10 Florida             
11 Georgia             
12 Hawaii              
13 Idaho               
14 Illinois            
15 Indiana             
16 Iowa                
17 Kansas              
18 Kentucky            
19 Louisiana           
20 Maine               
21 Maryland            
22 Massachusetts       
23 Michigan            
24 Minnesota           
25 Mississippi         
26 Missouri            
27 Montana             
28 Nebraska            
29 Nevada              
30 New Hampshire       
31 New Jersey          
32 New Mexico          
33 New York            
34 North Carolina      
35 North Dakota        
36 Ohio                
37 Oklahoma            
38 Oregon              
39 Pennsylvania        
40 Rhode Island        
41 South Carolina      
42 South Dakota        
43 Tennessee           
44 Texas               
45 Utah                
46 Vermont             
47 Virginia            
48 Washington          
49 West Virginia       
50 Wisconsin           
51 Wyoming             
52 D.C.

Now we will replace "D.C." with "District of Columbia" using str_replace(). We can use the tally() function of the dplyr package to check that we have fewer now.

poverty_rate_data %<>% 
mutate(STATE = str_replace(STATE, pattern = "D.C.", 
                              replacement = "District of Columbia" ))

poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  distinct(STATE) %>% tally()

# A tibble: 1 x 1
      n
  <int>
1    51

Great! Now are each of the states occurring as often as the unique year values? We can first check how many year values there are. Then can use the count() function of the dplyr package to check how often the states are repeated.

poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  tally()

# A tibble: 1 x 1
      n
  <int>
1    41

There are 41 different sets of data according to year values.

### Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  count(STATE) %>% 
  print(n = 51)

# A tibble: 51 x 2
   STATE                    n
   <chr>                <int>
 1 Alabama                 41
 2 Alaska                  41
 3 Arizona                 41
 4 Arkansas                41
 5 California              41
 6 Colorado                41
 7 Connecticut             41
 8 Delaware                41
 9 District of Columbia    41
10 Florida                 41
11 Georgia                 41
12 Hawaii                  41
13 Idaho                   41
14 Illinois                41
15 Indiana                 41
16 Iowa                    41
17 Kansas                  41
18 Kentucky                41
19 Louisiana               41
20 Maine                   41
21 Maryland                41
22 Massachusetts           41
23 Michigan                41
24 Minnesota               41
25 Mississippi             41
26 Missouri                41
27 Montana                 41
28 Nebraska                41
29 Nevada                  41
30 New Hampshire           41
31 New Jersey              41
32 New Mexico              41
33 New York                41
34 North Carolina          41
35 North Dakota            41
36 Ohio                    41
37 Oklahoma                41
38 Oregon                  41
39 Pennsylvania            41
40 Rhode Island            41
41 South Carolina          41
42 South Dakota            41
43 Tennessee               41
44 Texas                   41
45 Utah                    41
46 Vermont                 41
47 Virginia                41
48 Washington              41
49 West Virginia           41
50 Wisconsin               41
51 Wyoming                 41

Indeed, looks like each of the states are repeated the same number of times!

Now let’s create a new variable YEAR that repeats the year values for all of the different states and for the row that has just the year value for a total of 52.

year_values <- poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  distinct(STATE)

  year_values<-rep(pull(year_values, STATE), each = 52)
setequal(length(year_values), length(poverty_rate_data$STATE))

[1] TRUE

Now we will add this to our poverty_rate_data. We will also remove the length_state variable using the select() function of the dplyr package and a minus sign before the variable name.

poverty_rate_data %<>%
  mutate(year_value = year_values) %>%
  select(-length_state)

#Scroll through the output!
poverty_rate_data %>% print(n = 100)

# A tibble: 2,132 x 7
    STATE         Total Number Number_se Percent       Percent_se     year_value
    <chr>         <chr> <chr>  <chr>     <chr>         <chr>          <chr>     
  1 2018          <NA>  <NA>   <NA>      <NA>          <NA>           2018      
  2 Alabama       4877  779    65        16            1.3            2018      
  3 Alaska        720   94     9         13.1          1.2            2018      
  4 Arizona       7241  929    80        12.800000000… 1.10000000000… 2018      
  5 Arkansas      2912  462    38        15.9          1.3            2018      
  6 California    39150 4664   184       11.9          0.5            2018      
  7 Colorado      5739  521    51        9.0999999999… 0.90000000000… 2018      
  8 Connecticut   3413  348    43        10.199999999… 1.3            2018      
  9 Delaware      976   72     9         7.4000000000… 1              2018      
 10 District of … 692   102    8         14.699999999… 1.10000000000… 2018      
 11 Florida       21117 2883   173       13.699999999… 0.80000000000… 2018      
 12 Georgia       10423 1548   115       14.800000000… 1.10000000000… 2018      
 13 Hawaii        1393  128    16        9.1999999999… 1.10000000000… 2018      
 14 Idaho         1766  202    25        11.5          1.39999999999… 2018      
 15 Illinois      12589 1292   130       10.300000000… 1              2018      
 16 Indiana       6582  761    68        11.6          1              2018      
 17 Iowa          3110  277    30        8.9000000000… 1              2018      
 18 Kansas        2835  212    27        7.5           0.90000000000… 2018      
 19 Kentucky      4445  696    76        15.699999999… 1.7            2018      
 20 Louisiana     4510  858    38        19            0.80000000000… 2018      
 21 Maine         1321  153    19        11.6          1.39999999999… 2018      
 22 Maryland      6030  480    50        8             0.80000000000… 2018      
 23 Massachusetts 6883  601    59        8.6999999999… 0.90000000000… 2018      
 24 Michigan      9913  1036   78        10.5          0.80000000000… 2018      
 25 Minnesota     5746  456    47        7.9000000000… 0.80000000000… 2018      
 26 Mississippi   2899  567    27        19.600000000… 0.90000000000… 2018      
 27 Missouri      6026  745    81        12.4          1.3            2018      
 28 Montana       1041  107    12        10.300000000… 1.10000000000… 2018      
 29 Nebraska      1893  199    25        10.5          1.3            2018      
 30 Nevada        3008  390    37        13            1.2            2018      
 31 New Hampshire 1349  82     11        6.0999999999… 0.80000000000… 2018      
 32 New Jersey    8790  725    65        8.1999999999… 0.69999999999… 2018      
 33 New Mexico    2054  342    27        16.600000000… 1.3            2018      
 34 New York      19343 2145   110       11.1          0.59999999999… 2018      
 35 North Caroli… 10369 1355   96        13.1          0.90000000000… 2018      
 36 North Dakota  745   72     9         9.6999999999… 1.2            2018      
 37 Ohio          11452 1365   115       11.9          1              2018      
 38 Oklahoma      3858  518    48        13.4          1.2            2018      
 39 Oregon        4172  404    45        9.6999999999… 1.10000000000… 2018      
 40 Pennsylvania  12519 1476   124       11.800000000… 1              2018      
 41 Rhode Island  1036  92     15        8.9000000000… 1.39999999999… 2018      
 42 South Caroli… 5036  642    45        12.800000000… 0.90000000000… 2018      
 43 South Dakota  853   90     8         10.6          0.90000000000… 2018      
 44 Tennessee     6665  800    73        12            1.10000000000… 2018      
 45 Texas         28497 3894   173       13.699999999… 0.59999999999… 2018      
 46 Utah          3173  219    37        6.9000000000… 1.2            2018      
 47 Vermont       616   60     7         9.6999999999… 1.10000000000… 2018      
 48 Virginia      8393  821    86        9.8000000000… 1              2018      
 49 Washington    7555  647    101       8.5999999999… 1.3            2018      
 50 West Virginia 1762  279    21        15.9          1.2            2018      
 51 Wisconsin     5795  499    54        8.5999999999… 0.90000000000… 2018      
 52 Wyoming       565   53     6         9.5           1.10000000000… 2018      
 53 2017 (21)     <NA>  <NA>   <NA>      <NA>          <NA>           2017 (21) 
 54 Alabama       4801  735    58        15.300000000… 1.2            2017 (21) 
 55 Alaska        719   87     12        12.1          1.7            2017 (21) 
 56 Arizona       6981  951    104       13.6          1.5            2017 (21) 
 57 Arkansas      2921  436    33        14.9          1.10000000000… 2017 (21) 
 58 California    39247 4759   182       12.1          0.5            2017 (21) 
 59 Colorado      5527  489    51        8.9000000000… 0.90000000000… 2017 (21) 
 60 Connecticut   3553  377    43        10.6          1.2            2017 (21) 
 61 Delaware      967   85     10        8.8000000000… 1.10000000000… 2017 (21) 
 62 District of … 691   96     8         13.9          1.10000000000… 2017 (21) 
 63 Florida       20909 2809   173       13.4          0.80000000000… 2017 (21) 
 64 Georgia       10231 1339   104       13.1          1              2017 (21) 
 65 Hawaii        1402  149    17        10.6          1.2            2017 (21) 
 66 Idaho         1730  199    23        11.5          1.3            2017 (21) 
 67 Illinois      12597 1454   102       11.5          0.80000000000… 2017 (21) 
 68 Indiana       6532  762    70        11.699999999… 1.10000000000… 2017 (21) 
 69 Iowa          3053  230    33        7.5           1.10000000000… 2017 (21) 
 70 Kansas        2868  411    33        14.300000000… 1.2            2017 (21) 
 71 Kentucky      4395  591    76        13.5          1.7            2017 (21) 
 72 Louisiana     4535  931    48        20.5          1.10000000000… 2017 (21) 
 73 Maine         1315  163    19        12.4          1.39999999999… 2017 (21) 
 74 Maryland      5977  451    58        7.5999999999… 1              2017 (21) 
 75 Massachusetts 6784  763    71        11.199999999… 1              2017 (21) 
 76 Michigan      9895  1135   88        11.5          0.90000000000… 2017 (21) 
 77 Minnesota     5619  479    57        8.5           1              2017 (21) 
 78 Mississippi   2948  544    27        18.5          0.90000000000… 2017 (21) 
 79 Missouri      5988  683    81        11.4          1.39999999999… 2017 (21) 
 80 Montana       1041  107    11        10.300000000… 1              2017 (21) 
 81 Nebraska      1878  216    25        11.5          1.3            2017 (21) 
 82 Nevada        2979  392    37        13.199999999… 1.2            2017 (21) 
 83 New Hampshire 1333  95     13        7.2000000000… 1              2017 (21) 
 84 New Jersey    9015  894    95        9.9000000000… 1.10000000000… 2017 (21) 
 85 New Mexico    2035  402    28        19.699999999… 1.39999999999… 2017 (21) 
 86 New York      19735 2510   141       12.699999999… 0.69999999999… 2017 (21) 
 87 North Caroli… 10297 1567   96        15.199999999… 0.90000000000… 2017 (21) 
 88 North Dakota  740   92     11        12.4          1.5            2017 (21) 
 89 Ohio          11491 1479   98        12.9          0.90000000000… 2017 (21) 
 90 Oklahoma      3817  490    47        12.800000000… 1.2            2017 (21) 
 91 Oregon        4202  482    55        11.5          1.3            2017 (21) 
 92 Pennsylvania  12568 1373   94        10.9          0.80000000000… 2017 (21) 
 93 Rhode Island  1046  118    15        11.300000000… 1.39999999999… 2017 (21) 
 94 South Caroli… 4955  756    55        15.199999999… 1.10000000000… 2017 (21) 
 95 South Dakota  870   93     15        10.699999999… 1.7            2017 (21) 
 96 Tennessee     6699  759    65        11.300000000… 1              2017 (21) 
 97 Texas         28090 3715   186       13.199999999… 0.69999999999… 2017 (21) 
 98 Utah          3130  272    34        8.6999999999… 1.10000000000… 2017 (21) 
 99 Vermont       613   53     6         8.5999999999… 1              2017 (21) 
100 Virginia      8242  862    71        10.5          0.90000000000… 2017 (21) 
# … with 2,032 more rows

Looks good! Now we will remove the rows that have just the year values by only preserving those with alpha characters.

poverty_rate_data %<>%
    filter(str_detect(STATE, "[:alpha:]"))

Now let’s remove the older data for 2013 and 2017 which is the data that appears lower in the tibble.

poverty_rate_data %<>%
filter(year_value != "2017") %>%
filter(year_value != "2013 (18)")

We also want to just keep the first 4 digits of the year_value and create a YEAR variable. We need to pull the year_value data because str_sub() expects a character vector not a tibble.

poverty_rate_data %<>%
  mutate(YEAR = str_sub(pull(., year_value), start = 1, end=4))

poverty_rate_data

# A tibble: 1,989 x 8
   STATE       Total Number Number_se Percent     Percent_se    year_value YEAR 
   <chr>       <chr> <chr>  <chr>     <chr>       <chr>         <chr>      <chr>
 1 Alabama     4877  779    65        16          1.3           2018       2018 
 2 Alaska      720   94     9         13.1        1.2           2018       2018 
 3 Arizona     7241  929    80        12.8000000… 1.1000000000… 2018       2018 
 4 Arkansas    2912  462    38        15.9        1.3           2018       2018 
 5 California  39150 4664   184       11.9        0.5           2018       2018 
 6 Colorado    5739  521    51        9.09999999… 0.9000000000… 2018       2018 
 7 Connecticut 3413  348    43        10.1999999… 1.3           2018       2018 
 8 Delaware    976   72     9         7.40000000… 1             2018       2018 
 9 District o… 692   102    8         14.6999999… 1.1000000000… 2018       2018 
10 Florida     21117 2883   173       13.6999999… 0.8000000000… 2018       2018 
# … with 1,979 more rows

Looks good! Now we will just remove the extra variables and rename the variables we want to keep to be similar to our other data.

poverty_rate_data %<>%
  dplyr::select(- Number,
                - Number_se,
                - Percent_se,
                - Total,
                - year_value) %>%
  rename("VALUE" = Percent) %>%
  mutate(VARIABLE = "Poverty_rate",
         YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))
head(poverty_rate_data)

# A tibble: 6 x 4
  STATE      VALUE  YEAR VARIABLE    
  <chr>      <dbl> <dbl> <chr>       
1 Alabama     16    2018 Poverty_rate
2 Alaska      13.1  2018 Poverty_rate
3 Arizona     12.8  2018 Poverty_rate
4 Arkansas    15.9  2018 Poverty_rate
5 California  11.9  2018 Poverty_rate
6 Colorado     9.1  2018 Poverty_rate

Looks great! AVOCADO this data is per 1000k? I think I need to restrict to 2014 to match the other data and thats why I get an error when making the Donohue_df

From Michael:

poverty_rate_data <-poverty_rate_data2
notes <- 4

poverty_rate_data2 <- poverty_rate_data[-((dim(poverty_rate_data)[1]-notes+1):dim(poverty_rate_data)[1]),]

states_eq <- 51

extra_col <- 2

rep_rows <- states_eq + extra_col

groups <- (dim(poverty_rate_data)[1])/(rep_rows)

paste(groups - (2018-1980 + 1), "extra groups")

poverty_rate_data$year_group <- rep(1:groups, each=rep_rows)

poverty_rate_data <- poverty_rate_data %>%
  group_by(year_group) %>%
  group_split()

head(poverty_rate_data[[1]])

poverty_rate_data <- poverty_rate_data %>%
  map(~mutate(.,
              row_id = row_number())) %>%
  map(~filter(.,row_id != 2)) %>%
  map(~dplyr::select(.,-row_id))

poverty_rate_data_names <- poverty_rate_data %>%
  sapply(., "[",1,1, drop=TRUE) %>%
  str_replace_all(.,"[:space:]","_")

names(poverty_rate_data) <- poverty_rate_data_names

# Recall 2 extra groups. 
# footnotes available at https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-footnotes/cps-historic-footnotes.html

poverty_rate_data$`2017_(21)` <- NULL

poverty_rate_data$`2013_(19)` <- NULL

poverty_rate_data_names <- poverty_rate_data %>%
  sapply(., "[",1,1, drop=TRUE) %>%
  str_sub(., start = 1, end=4)

names(poverty_rate_data) <- poverty_rate_data_names

poverty_rate_data <- poverty_rate_data %>%
  map_df(bind_rows, .id = "YEAR") %>%
  dplyr::select(-year_group)

poverty_rate_data <- poverty_rate_data %>%
    mutate(n_na = rowSums(is.na(.))) 

# This shows that there is systematic missing values stemmingly *solely* from the rows without poverty data and only a label designating the year
poverty_rate_data %>% 
  group_by(n_na) %>%
  tally()

sapply(poverty_rate_data, class)



colnames(poverty_rate_data)

Violent crime

Click here to see details about how the violent crime data was wrangled

The crime_data was importated using read_lines() and we have some lines that we don’t necessarily need. A large amount of the original data is notes at the end of the table. We want to remove these lines. We can determine where they start by searching for the row that contains the first statement of these notes using the str_which() function of the stringr package. We will subtract one from this as there is a blank line in between.

tail(crime_data)

[1] ",,\"Vermont - The state UCR Program was unable to provide complete 1997 offense figures in accordance with UCR guidelines.  The 1996 and 1997 percent changes within the geographic division in which Vermont is categorized were applied to the valid 1996 state total to effect the 1997 state total.\""                                                                                                              
[2] "  "                                                                                                                                                                                                                                                                                                                                                                                                                     
[3] " "                                                                                                                                                                                                                                                                                                                                                                                                                      
[4] ",,\"Wisconsin - The state UCR Program was unable to provide complete 1998 offense figures in accordance with UCR guidelines.  The state total was estimated by using 1997 figures for the nonreporting areas and applying 1997 versus 1998 percentage changes for the division in which Wisconsin is located.  The estimates for the nonreporting areas were then increased by any actual 1998 crime counts received.\""
[5] " "                                                                                                                                                                                                                                                                                                                                                                                                                      
[6] "\"Sources: FBI, Uniform Crime Reports, prepared by the National Archive of Criminal Justice Data\" "

crime_data <- crime_data[-((str_which(crime_data, "The figures shown in this column for the offense of rape were estimated using the legacy UCR definition of rape")-1): length(crime_data))]
#crime_data <- crime_data[-(2143:length(crime_data))]
tail(crime_data)

[1] "2009,     544270,       1196,          11,         172,,          78,         935 "            
[2] "2010,     564554,       1117,           8,         162,,          77,         870 "            
[3] "2011,     567356,       1245,          18,         146,,          71,       1010 "             
[4] "2012,     576626,       1161,          14,         154,,          61,         932 "            
[5] "2013,     583223,       1212,          17,         144,         204,          74,         917 "
[6] "2014,     584153,       1142,          16,         126,         174,          53,         899 "

There are lines for each year from 1977 to 2014 as well as four lines about each state and the header information for each state. Here you can see what the orginal data looks like:

knitr::include_graphics(here("img", "crime_data.png"))

We want to delete the header information and only retain the lines numeric values or state names. Thus since there are 38 years worth of data for each state and 4 lines for each header, then each state has 42 lines. We want to delete the lines between and including line 2 to 4 for each state. We will save the header information once to use later.

head(crime_data)

[1] "Estimated crime in Alabama"                                                                                                           
[2] "\n,,National or state crime,,,,,,,"                                                                                                   
[3] "\n,,Violent crime,,,,,,,"                                                                                                             
[4] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"
[5] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "                                                      
[6] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "

x <- 2014-1977+1
rep_cycle <- 4 + x
rep_cycle_cut <- 2 + x
colnames_crime<-(crime_data[4])

So starting at line 2 and and 3 and 4 we create a sequence of numbers that increase by the number of rows of the length of the individual state data. We can do so using the base seq() function. We can take a look at these in order using the base sort() function.

delete_rows <- c(seq(from = 2,
                       to = length(crime_data),
                       by = rep_cycle),
                 seq(from = 3,
                       to = length(crime_data),
                       by = rep_cycle), 
                 seq(from = 4,
                       to = length(crime_data),
                       by = rep_cycle))
sort(delete_rows)

  [1]    2    3    4   44   45   46   86   87   88  128  129  130  170  171  172
 [16]  212  213  214  254  255  256  296  297  298  338  339  340  380  381  382
 [31]  422  423  424  464  465  466  506  507  508  548  549  550  590  591  592
 [46]  632  633  634  674  675  676  716  717  718  758  759  760  800  801  802
 [61]  842  843  844  884  885  886  926  927  928  968  969  970 1010 1011 1012
 [76] 1052 1053 1054 1094 1095 1096 1136 1137 1138 1178 1179 1180 1220 1221 1222
 [91] 1262 1263 1264 1304 1305 1306 1346 1347 1348 1388 1389 1390 1430 1431 1432
[106] 1472 1473 1474 1514 1515 1516 1556 1557 1558 1598 1599 1600 1640 1641 1642
[121] 1682 1683 1684 1724 1725 1726 1766 1767 1768 1808 1809 1810 1850 1851 1852
[136] 1892 1893 1894 1934 1935 1936 1976 1977 1978 2018 2019 2020 2060 2061 2062
[151] 2102 2103 2104

Thus we will delete lines 2, 3, and 4 and then skip 40 lines (to account for the state information for the first state, the lines of information for the 38 years, and then the state information for the next state) and then delete the next 3 consecutive lines and so on. We can indeed see that line 44-46 are what we wish to remove.

crime_data[44:46]

[1] "\n,,National or state crime,,,,,,,"                                                                                                   
[2] "\n,,Violent crime,,,,,,,"                                                                                                             
[3] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"

crime_data <- crime_data[-delete_rows]

Nice!

Now we can select all the lines that have state information. We can repeat each of these for the 38 years for each state as well as this line that contains the state information by using the base rep() function with the each = argument. Finally we will remove the "Estimated crime in " portion of the string using the str_remove() function of the stringr package. We will later combine this with the crime data.

state_label_order <-crime_data[str_which(crime_data, "Estimated crime")]
state_label_order

 [1] "Estimated crime in Alabama"             
 [2] "Estimated crime in Alaska"              
 [3] "Estimated crime in Arizona"             
 [4] "Estimated crime in Arkansas"            
 [5] "Estimated crime in California"          
 [6] "Estimated crime in Colorado"            
 [7] "Estimated crime in Connecticut"         
 [8] "Estimated crime in Delaware"            
 [9] "Estimated crime in District of Columbia"
[10] "Estimated crime in Florida"             
[11] "Estimated crime in Georgia"             
[12] "Estimated crime in Hawaii"              
[13] "Estimated crime in Idaho"               
[14] "Estimated crime in Illinois"            
[15] "Estimated crime in Indiana"             
[16] "Estimated crime in Iowa"                
[17] "Estimated crime in Kansas"              
[18] "Estimated crime in Kentucky"            
[19] "Estimated crime in Louisiana"           
[20] "Estimated crime in Maine"               
[21] "Estimated crime in Maryland"            
[22] "Estimated crime in Massachusetts"       
[23] "Estimated crime in Michigan"            
[24] "Estimated crime in Minnesota"           
[25] "Estimated crime in Mississippi"         
[26] "Estimated crime in Missouri"            
[27] "Estimated crime in Montana"             
[28] "Estimated crime in Nebraska"            
[29] "Estimated crime in Nevada"              
[30] "Estimated crime in New Hampshire"       
[31] "Estimated crime in New Jersey"          
[32] "Estimated crime in New Mexico"          
[33] "Estimated crime in New York"            
[34] "Estimated crime in North Carolina"      
[35] "Estimated crime in North Dakota"        
[36] "Estimated crime in Ohio"                
[37] "Estimated crime in Oklahoma"            
[38] "Estimated crime in Oregon"              
[39] "Estimated crime in Pennsylvania"        
[40] "Estimated crime in Rhode Island"        
[41] "Estimated crime in South Carolina"      
[42] "Estimated crime in South Dakota"        
[43] "Estimated crime in Tennessee"           
[44] "Estimated crime in Texas"               
[45] "Estimated crime in Utah"                
[46] "Estimated crime in Vermont"             
[47] "Estimated crime in Virginia"            
[48] "Estimated crime in Washington"          
[49] "Estimated crime in West Virginia"       
[50] "Estimated crime in Wisconsin"           
[51] "Estimated crime in Wyoming"

state_label_order <- rep(state_label_order, each = 38)

crime_states <-str_remove(state_label_order, pattern = "Estimated crime in ")
head(crime_states)

[1] "Alabama" "Alabama" "Alabama" "Alabama" "Alabama" "Alabama"

Nice! Now for the rest of the data. We now need to remove the lines with the state information.

crime_data <-crime_data[-str_which(crime_data, "Estimated crime")]
head(crime_data)

[1] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "
[2] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "
[3] "1979,   3769000,      15578,         496,       1037,,       4127,       9918 " 
[4] "1980,   3861466,      17320,         509,       1158,,       5102,      10551 " 
[5] "1981,   3916000,      18423,         465,       1021,,       4952,      11985 " 
[6] "1982,   3943000,      17653,         417,       1026,,       4417,      11793 "

tail(crime_data)

[1] "2009,     544270,       1196,          11,         172,,          78,         935 "            
[2] "2010,     564554,       1117,           8,         162,,          77,         870 "            
[3] "2011,     567356,       1245,          18,         146,,          71,       1010 "             
[4] "2012,     576626,       1161,          14,         154,,          61,         932 "            
[5] "2013,     583223,       1212,          17,         144,         204,          74,         917 "
[6] "2014,     584153,       1142,          16,         126,         174,          53,         899 "

It appears that the data is comma separted with 8 columns. One of the middle columns often has no values, we need to fill these in with NAs. We can use the read_csv() fucntion from the readr package to do this. It turns out you don’t have to have a file, but you can also use a string our a vector.

crime_data_sep <-read_csv(crime_data, col_names = FALSE)
head(crime_data)

[1] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "
[2] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "
[3] "1979,   3769000,      15578,         496,       1037,,       4127,       9918 " 
[4] "1980,   3861466,      17320,         509,       1158,,       5102,      10551 " 
[5] "1981,   3916000,      18423,         465,       1021,,       4952,      11985 " 
[6] "1982,   3943000,      17653,         417,       1026,,       4417,      11793 "

Nice! Now we just need our colnames. Recall that we saved this information.

colnames_crime

[1] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"

colnames(crime_data_sep) <-c("Year",
                             "Population",
                             "Violent_crime_total",
                             "Murder_and_nonnegligent_Manslaughter",
                             "Legacy_rape" ,
                             "Revised_rape", 
                             "Robbery",
                             "Aggravated_assault")
head(crime_data_sep)

# A tibble: 6 x 8
   Year Population Violent_crime_t… Murder_and_nonn… Legacy_rape Revised_rape
  <dbl>      <dbl>            <dbl>            <dbl>       <dbl>        <dbl>
1  1977    3690000            15293              524         929           NA
2  1978    3742000            15682              499         954           NA
3  1979    3769000            15578              496        1037           NA
4  1980    3861466            17320              509        1158           NA
5  1981    3916000            18423              465        1021           NA
6  1982    3943000            17653              417        1026           NA
# … with 2 more variables: Robbery <dbl>, Aggravated_assault <dbl>

We also want to combine this with the state information we collected earlier. We will use the bind_cols() function of the dplyr package to do this. This requires that the data have the same number of rows.

crime_data_sep <-bind_cols(STATE =crime_states, 
          crime_data_sep)

Now we will rename the Viol_crime_count variable to be Variable and we will remove all of the other columns except for Year. We will also rename the variables to look like the other datasets.

crime_data <- crime_data_sep %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = Violent_crime_total) %>%
  rename("YEAR" = Year) %>%
  select(YEAR,STATE, VARIABLE, VALUE)

crime_data

# A tibble: 1,938 x 4
    YEAR STATE   VARIABLE         VALUE
   <dbl> <chr>   <chr>            <dbl>
 1  1977 Alabama Viol_crime_count 15293
 2  1978 Alabama Viol_crime_count 15682
 3  1979 Alabama Viol_crime_count 15578
 4  1980 Alabama Viol_crime_count 17320
 5  1981 Alabama Viol_crime_count 18423
 6  1982 Alabama Viol_crime_count 17653
 7  1983 Alabama Viol_crime_count 16471
 8  1984 Alabama Viol_crime_count 17204
 9  1985 Alabama Viol_crime_count 18398
10  1986 Alabama Viol_crime_count 22616
# … with 1,928 more rows

from Michael:

crime_data2 <- data.frame(cbind(crime_data2, rep(1:(length(crime_data2)/rep_cycle_cut),each=rep_cycle_cut)))

colnames(crime_data) <- c("String","STATE_GROUP")

crime_data <- crime_data %>%
  group_by(STATE_GROUP) %>%
  group_split()

columns_crime_data <- 8

crime_data <- crime_data %>%
  map(~mutate(.,
               State = case_when(str_detect(String, "Estimated crime in ") ~ substring(String, nchar("Estimated crime in ")+1)),
              row_id = row_number())) %>%
  map(~fill(., State)) %>%
  map(~filter(.,row_id > 2)) %>%
  map(~mutate(.,
              String = paste0(String, ",", State))) %>%
  map(~dplyr::select(.,String)) %>%
  map(~str_split_fixed(.$String,",",columns_crime_data + 1)) %>%
  map(~data.frame(.)) %>%
  map(~rename(.,"YEAR"=X1,
              "Extra_col1"=X2,
              "VC"=X3,
              "Extra_col2"=X4,
              "Extra_col3"=X5,
              "Extra_col4"=X6,
              "Extra_col5"=X7,
              "Extra_col6"=X8,
              "STATE"=X9)) %>%
  map(~dplyr::select(.,-contains("Extra_col"))) %>%
  map(~.x %>% mutate_all(~trimws(.,which = "both"))) %>%
  map_df(bind_rows)

sapply(crime_data, class)

crime_data <- crime_data %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = VC) %>%
  as.tibble() %>%
  mutate(YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))

RTC laws

Click here to see details about how the RTC Law data was wrangled

The information about the laws for each state are located on page 62 of the Donohue, et al. article, so first we will select just this page. We can print part of the character string for this page using the utils str() function and the ncar.max argument.

DAWpaper_p_62 <- DAWpaper[[62]]
str(DAWpaper_p_62, nchar.max = 1000)

 chr "                                         Table A1: RTC Adoption Dates\n         State       Effective Date of RTC Law Fraction of Year In Effect Year of Passage RTC Date (Synthetic Controls Analysis)\n      Alabama                    1975                                                                    1975\n       Alaska                 10/1/1994                          0.252                                   1995\n       Arizona                7/17/1994                          0.460                                   1995\n      Arkansas                7/27/1995                          0.433                                   1996\n     California                  N/A                                                                        0\n      Colorado                5/17/2003                          0.627                                   2003\n    Connecticut                  1970                                                                    1970\n     "| __truncated__

We can also use the cat function to see the data printed nicely to see what we are going for.

cat(DAWpaper_p_62)

                                         Table A1: RTC Adoption Dates
         State       Effective Date of RTC Law Fraction of Year In Effect Year of Passage RTC Date (Synthetic Controls Analysis)
      Alabama                    1975                                                                    1975
       Alaska                 10/1/1994                          0.252                                   1995
       Arizona                7/17/1994                          0.460                                   1995
      Arkansas                7/27/1995                          0.433                                   1996
     California                  N/A                                                                        0
      Colorado                5/17/2003                          0.627                                   2003
    Connecticut                  1970                                                                    1970
      Delaware                   N/A                                                                        0
District of Columbia             N/A                                                                        0
       Florida                10/1/1987                          0.252                                   1988
       Georgia                8/25/1989                          0.353                                   1990
       Hawaii                    N/A                                                                        0
        Idaho                  7/1/1990                          0.504                                   1990
       Illinois                1/5/2014                                                                  2014
       Indiana                1/15/1980                          0.962                                   1980
         Iowa                  1/1/2011                          1.000                                   2011
       Kansas                  1/1/2007                          1.000                                   2007
      Kentucky                10/1/1996                          0.251                                   1997
      Louisiana               4/19/1996                          0.702                                   1996
        Maine                 9/19/1985                          0.285                                   1986
      Maryland                   N/A                                                                        0
   Massachusetts                 N/A                                                                        0
      Michigan                 7/1/2001                          0.504                                   2001
     Minnesota                5/28/2003                          0.597                                   2003
     Mississippi               7/1/1990                          0.504                                   1990
      Missouri                2/26/2004                          0.847                                   2004
      Montana                 10/1/1991                          0.252                                   1992
      Nebraska                 1/1/2007                          1.000                                   2007
       Nevada                 10/1/1995                          0.252                                   1996
  New Hampshire                  1959                                                                    1959
     New Jersey                  N/A                                                                        0
    New Mexico                 1/1/2004                          1.000                                   2004
     New York                    N/A                                                                        0
  North Carolina              12/1/1995                          0.085                                   1996
   North Dakota                8/1/1985                          0.419                                   1986
         Ohio                  4/8/2004                          0.732                                   2004
     Oklahoma                  1/1/1996                          1.000                                   1996
       Oregon                  1/1/1990                          1.000                                   1990
   Pennsylvania               6/17/1989                          0.542                                   1989
    Philadelphia             10/11/1995                          0.225                                   1996
   Rhode Island                  N/A                                                                        0
  South Carolina              8/23/1996                          0.358                                   1997
   South Dakota                7/1/1985                          0.504                                   1985
     Tennessee                10/1/1996                          0.251                                   1997
        Texas                  1/1/1996                          1.000                                   1996
         Utah                  5/1/1995                          0.671                                   1995
      Vermont                    1970                                                                    1970
       Virginia                5/5/1995                          0.660                                   1995
    Washington                   1961                                                                    1961
   West Virginia               7/7/1989                          0.488                                   1990
     Wisconsin                11/1/2011                          0.167                                   2012
      Wyoming                 10/1/1994                          0.252                                   1995
                                                             60

We can see that this is one continuous character string. We can sparate into lines based on the presence of "\n" in the string using the str_split() function of the stringr package. We need to unlist the data first, as the output of str_split() is a list. Finally, we can convert it to a tibble using the as.tibble() function of the tibble package. We also see that we don’t need the first line about the table. We can remove this with the slice() function of the dplyr package. We can also use this to remove the colnames so that we can replace them. Thus we will use slice(-(1:2)) to remove the first two lines.

So we will split and unlist() the data.

p_62 <- DAWpaper_p_62 %>%
    str_split("\n") %>%
    unlist() %>%
    dplyr::as_tibble() %>%
    slice(-(1:2))

head(p_62)

# A tibble: 6 x 1
  value                                                                         
  <chr>                                                                         
1 "      Alabama                    1975                                       …
2 "       Alaska                 10/1/1994                          0.252      …
3 "       Arizona                7/17/1994                          0.460      …
4 "      Arkansas                7/27/1995                          0.433      …
5 "     California                  N/A                                        …
6 "      Colorado                5/17/2003                          0.627      …

tail(p_62)

# A tibble: 6 x 1
  value                                                                         
  <chr>                                                                         
1 "    Washington                   1961                                       …
2 "   West Virginia               7/7/1989                          0.488      …
3 "     Wisconsin                11/1/2011                          0.167      …
4 "      Wyoming                 10/1/1994                          0.252      …
5 "                                                             60"             
6 ""

We also see by looking at the tail that we want to remove the last two lines. One is empty and the other has only 63 characters, which is the line with the page number.

p_62 %<>%
  rename(RTC = value)
p_62 %>%
  mutate(RTC = map_chr(RTC, str_length)) %>%tail()

# A tibble: 6 x 1
  RTC  
  <chr>
1 109  
2 109  
3 109  
4 109  
5 63   
6 0

p_62[53,] # physcial page 60

# A tibble: 1 x 1
  RTC                                                              
  <chr>                                                            
1 "                                                             60"

p_62[54,] # empty line

# A tibble: 1 x 1
  RTC  
  <chr>
1 ""

p_62 %<>%
    slice(-c(53:54))

Now we will try splitting by spaces. We can show the output withe the first() and nth() functions of the dplyr package.

p_62 %>% pull(RTC) %>% map(str_split, pattern = " ") %>% first()

[[1]]
 [1] ""        ""        ""        ""        ""        ""        "Alabama"
 [8] ""        ""        ""        ""        ""        ""        ""       
[15] ""        ""        ""        ""        ""        ""        ""       
[22] ""        ""        ""        ""        ""        "1975"    ""       
[29] ""        ""        ""        ""        ""        ""        ""       
[36] ""        ""        ""        ""        ""        ""        ""       
[43] ""        ""        ""        ""        ""        ""        ""       
[50] ""        ""        ""        ""        ""        ""        ""       
[57] ""        ""        ""        ""        ""        ""        ""       
[64] ""        ""        ""        ""        ""        ""        ""       
[71] ""        ""        ""        ""        ""        ""        ""       
[78] ""        ""        ""        ""        ""        ""        ""       
[85] ""        ""        ""        ""        ""        ""        ""       
[92] ""        ""        ""        "1975"

p_62 %>% pull(RTC) %>% map(str_split, pattern = " ") %>% nth( 5)

[[1]]
 [1] ""           ""           ""           ""           ""          
 [6] "California" ""           ""           ""           ""          
[11] ""           ""           ""           ""           ""          
[16] ""           ""           ""           ""           ""          
[21] ""           ""           ""           "N/A"        ""          
[26] ""           ""           ""           ""           ""          
[31] ""           ""           ""           ""           ""          
[36] ""           ""           ""           ""           ""          
[41] ""           ""           ""           ""           ""          
[46] ""           ""           ""           ""           ""          
[51] ""           ""           ""           ""           ""          
[56] ""           ""           ""           ""           ""          
[61] ""           ""           ""           ""           ""          
[66] ""           ""           ""           ""           ""          
[71] ""           ""           ""           ""           ""          
[76] ""           ""           ""           ""           ""          
[81] ""           ""           ""           ""           ""          
[86] ""           ""           ""           ""           ""          
[91] ""           ""           ""           ""           ""          
[96] "0"

Interesting, we can see that there are lots of spaces between the elements of the table and that they vary by line. For example there are 6 spaces before Alabama and 7 spaces before Alaska.

Overall, that didn’t work quite like we expected.

Recall from the cheatsheet that "\\s" indicates a space. There are also ways to specify how many spaces using curly brackets{}.

knitr::include_graphics(here("img", "regex.png"))

knitr::include_graphics(here("img", "quantifiers.png"))

The spacing appears to vary quite a bit. WE can use the str_count() function of the stringr package to see how often we have white spaces larger than 5, 10, 15, or 40 spaces.

# how often are there white spaces with more than 5 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{5,}") %>% 
  unlist()

 [1] 3 4 4 4 3 4 2 3 2 4 4 3 4 3 4 4 4 4 4 4 3 2 4 4 4 4 4 4 4 2 3 3 3 3 3 4 4 4
[39] 3 3 2 3 3 4 4 4 3 4 2 3 4 4

# how often are there white spaces with more than 10 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{10,}") %>%
  unlist()

 [1] 2 3 3 3 2 3 2 2 2 3 3 2 3 2 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 3
[39] 3 3 2 3 3 3 3 3 2 3 2 3 3 3

# how often are there white spaces with more than 15 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{15,}") %>%
  unlist()

 [1] 2 3 3 3 2 3 2 2 1 3 3 2 3 2 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 2 2 3 2 2 3 3 3 3
[39] 3 2 2 2 3 3 3 3 2 3 2 3 3 3

# how often are there white spaces with more than 40 spaces
p_62 %>% 
  pull(RTC) %>%
  map(str_count, pattern = "\\s{40,}") %>% 
  unlist()

 [1] 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0
[39] 0 0 1 0 0 0 0 0 1 0 1 0 0 0

Rows with white spaces with more than 40 consecutive spaces is less common. It appears to be the case in the 1st and 5th row.

If we take a look at those rows we can see that this occurs when we have a missing value.

cat(DAWpaper_p_62)

                                         Table A1: RTC Adoption Dates
         State       Effective Date of RTC Law Fraction of Year In Effect Year of Passage RTC Date (Synthetic Controls Analysis)
      Alabama                    1975                                                                    1975
       Alaska                 10/1/1994                          0.252                                   1995
       Arizona                7/17/1994                          0.460                                   1995
      Arkansas                7/27/1995                          0.433                                   1996
     California                  N/A                                                                        0
      Colorado                5/17/2003                          0.627                                   2003
    Connecticut                  1970                                                                    1970
      Delaware                   N/A                                                                        0
District of Columbia             N/A                                                                        0
       Florida                10/1/1987                          0.252                                   1988
       Georgia                8/25/1989                          0.353                                   1990
       Hawaii                    N/A                                                                        0
        Idaho                  7/1/1990                          0.504                                   1990
       Illinois                1/5/2014                                                                  2014
       Indiana                1/15/1980                          0.962                                   1980
         Iowa                  1/1/2011                          1.000                                   2011
       Kansas                  1/1/2007                          1.000                                   2007
      Kentucky                10/1/1996                          0.251                                   1997
      Louisiana               4/19/1996                          0.702                                   1996
        Maine                 9/19/1985                          0.285                                   1986
      Maryland                   N/A                                                                        0
   Massachusetts                 N/A                                                                        0
      Michigan                 7/1/2001                          0.504                                   2001
     Minnesota                5/28/2003                          0.597                                   2003
     Mississippi               7/1/1990                          0.504                                   1990
      Missouri                2/26/2004                          0.847                                   2004
      Montana                 10/1/1991                          0.252                                   1992
      Nebraska                 1/1/2007                          1.000                                   2007
       Nevada                 10/1/1995                          0.252                                   1996
  New Hampshire                  1959                                                                    1959
     New Jersey                  N/A                                                                        0
    New Mexico                 1/1/2004                          1.000                                   2004
     New York                    N/A                                                                        0
  North Carolina              12/1/1995                          0.085                                   1996
   North Dakota                8/1/1985                          0.419                                   1986
         Ohio                  4/8/2004                          0.732                                   2004
     Oklahoma                  1/1/1996                          1.000                                   1996
       Oregon                  1/1/1990                          1.000                                   1990
   Pennsylvania               6/17/1989                          0.542                                   1989
    Philadelphia             10/11/1995                          0.225                                   1996
   Rhode Island                  N/A                                                                        0
  South Carolina              8/23/1996                          0.358                                   1997
   South Dakota                7/1/1985                          0.504                                   1985
     Tennessee                10/1/1996                          0.251                                   1997
        Texas                  1/1/1996                          1.000                                   1996
         Utah                  5/1/1995                          0.671                                   1995
      Vermont                    1970                                                                    1970
       Virginia                5/5/1995                          0.660                                   1995
    Washington                   1961                                                                    1961
   West Virginia               7/7/1989                          0.488                                   1990
     Wisconsin                11/1/2011                          0.167                                   2012
      Wyoming                 10/1/1994                          0.252                                   1995
                                                             60

So we will replace white spaces with more than 40 consecutive spaces with NA. Let’s also remove the leading white spaces that varies infront of the state names, as DC does not have any and this could cause a problem later. We will also replace any white spaces of 2 consecutive spaces or more , but less than 15 white spaces with “|” so that we can split the data based on this symbol. Thus we will also put these around the NA value that we are using replace the white spaces made of 40+ spaces.

p_62b <-p_62 %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{40,}", "|N/A|")) %>%
  mutate(RTC =str_trim(pull(., RTC), side = "left")) %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{2,15}", "|"))
head(p_62b)

# A tibble: 6 x 1
  RTC                              
  <chr>                            
1 Alabama||1975|N/A|1975           
2 Alaska||10/1/1994||0.252|||1995  
3 Arizona| 7/17/1994||0.460|||1995 
4 Arkansas| 7/27/1995||0.433|||1996
5 California||N/A|N/A|0            
6 Colorado| 5/17/2003||0.627|||2003

Now anytime there is one or more "|" we should have a column break. So now we will split the data by this symbol.

p_62b <-pull(p_62b, RTC) %>%
  str_split( "\\|{1,}") 

head(p_62b)

[[1]]
[1] "Alabama" "1975"    "N/A"     "1975"   

[[2]]
[1] "Alaska"    "10/1/1994" "0.252"     "1995"     

[[3]]
[1] "Arizona"    " 7/17/1994" "0.460"      "1995"      

[[4]]
[1] "Arkansas"   " 7/27/1995" "0.433"      "1996"      

[[5]]
[1] "California" "N/A"        "N/A"        "0"         

[[6]]
[1] "Colorado"   " 5/17/2003" "0.627"      "2003"

Great! Now we want to put our data in tibble format. To do so we need to bind the rows together. We can do so using the base rbind() function. We will use this instead of the bind_rows() function of dplyr because rbind() is less restrictive and allows for columns without names. We will use the base do.call() function, so that this is performed along each character string within the list of p_62b while maintaining the structure. Then we create a tibble out of this. avocado… describe do.call better… maybe do something tidyverse…

p_62 <- as.tibble(do.call(rbind, p_62b))

colnames(p_62) <- c("STATE",
                    "E_Date_RTC",
                    "Frac_Yr_Eff_Yr_Pass",
                    "RTC_Date_SA")

p_62 <- p_62 %>%
  dplyr::select(STATE, RTC_Date_SA) %>%
  rename("RTC_LAW_YEAR"= RTC_Date_SA) %>%
  mutate(RTC_LAW_YEAR = as.numeric(RTC_LAW_YEAR)) %>%
  mutate(RTC_LAW_YEAR = case_when(RTC_LAW_YEAR == 0 ~ Inf,
                              TRUE ~ RTC_LAW_YEAR))

RTC <-p_62
RTC

# A tibble: 52 x 2
   STATE                RTC_LAW_YEAR
   <chr>                       <dbl>
 1 Alabama                      1975
 2 Alaska                       1995
 3 Arizona                      1995
 4 Arkansas                     1996
 5 California                    Inf
 6 Colorado                     2003
 7 Connecticut                  1970
 8 Delaware                      Inf
 9 District of Columbia          Inf
10 Florida                      1988
# … with 42 more rows

avocado why inf?

from Michael

p_62b <-pull(p_62b, RTC) %>%
  str_split( "\\|{1,}") %>% as.data.frame()
  unlist()



p_62b <-p_62 %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{40,}", "|N/A|")) %>%
   mutate(RTC = str_replace_all(pull(., RTC), "\\s{2,15}", "|"))

p_62b %<>%
mutate(value = str_split(pull(., value), "\\|{1}"))

read_csv(p_62b$value)  

p_62 <- p_62 %>%
    apply(1,str_replace_all, "\\s{40,}", "|N/A|") %>%
    str_replace_all("\\s{2,15}", "|") %>%
    as.data.frame()

p_62 <- sapply(p_62$., str_split, "\\|{1,}")

sapply(p_62, nchar)

p_62 <- lapply(p_62, function(x) x[nchar(x) > 0]) 

p_62 <- as.data.frame(do.call(rbind, p_62))

rownames(p_62)

rownames(p_62) <- c()

colnames(p_62) <- c("STATE",
                    "E_Date_RTC",
                    "Frac_Yr_Eff_Yr_Pass",
                    "RTC_Date_SA")
sapply(p_62, class)

p_62 <- p_62 %>%
  dplyr::select(STATE, RTC_Date_SA) %>%
  rename("RTC_LAW_YEAR"=RTC_Date_SA) %>%
  mutate(RTC_LAW_YEAR = as.numeric(RTC_LAW_YEAR)) %>%
  mutate(RTC_LAW_YEAR = case_when(RTC_LAW_YEAR == 0 ~ Inf,
                              TRUE ~ RTC_LAW_YEAR))

sapply(p_62, class)

head(p_62)

Joining Data

Now we will join the data from the different data sets together to create a tibble of data for an analysis that will be similar to the data used by Donohue et al. {target="_blank"} and Lott and Mustard.

First we need to check that our data is indeed ready to be joined. We need to make sure that the column names are the same for each dataset that we intend to combine together.

We will use the compare_df_cols() and compare_df_cols_same() functions of the janitor package, to ensure that the column names are the same and that the column values are the same type so that the tibbles can be joined by row.

If they can be joined by row, then compare_df_cols_same() returns the value TRUE, while compare_df_cols(), provides a description of the columns.

library(janitor)

data_list <-  list(dem_DONOHUE,
                dem_LOTT,
                population_data,
                ue_rate_data,
                poverty_rate_data,
                crime_data,
                ps_data) #police staffing

janitor::compare_df_cols_same(data_list)

[1] TRUE

janitor::compare_df_cols(data_list)

  column_name data_list_1 data_list_2 data_list_3 data_list_4 data_list_5
1       STATE   character   character   character   character   character
2       VALUE     numeric     numeric     numeric     numeric     numeric
3    VARIABLE   character   character   character   character   character
4        YEAR     numeric     numeric     numeric     numeric     numeric
  data_list_6 data_list_7
1   character   character
2     numeric     numeric
3   character   character
4     numeric     numeric

checkstate <- function(x) { x %<>% distinct(STATE) %>% tally() %>% pull(n) }
map(data_list, checkstate)

[[1]]
[1] 51

[[2]]
[1] 51

[[3]]
[1] 51

[[4]]
[1] 51

[[5]]
[1] 51

[[6]]
[1] 51

[[7]]
[1] 51

checkyear <- function(x) { x %<>% distinct(YEAR) %>% tally() %>% pull(n) }
map(data_list, checkyear)

[[1]]
[1] 34

[[2]]
[1] 34

[[3]]
[1] 34

[[4]]
[1] 44

[[5]]
[1] 39

[[6]]
[1] 38

[[7]]
[1] 38

Avocado bind_rows will make na values for the later years beyond 2010 for the datasets that have them… not sure why we are adding these… maybe there is more about that later?

Donohue, et al.

We will now bind the demogrpahic data that we made for the Donohue-like analysis called dem_DONOHUE, as well as all the other datasets that we have wrangled. This is possible because we have the same colnames for each dataset. We will also use the pivot_wider() function of the tidyr package to change the shape of the data. This will make the data have more columns. Each unique value in the column called VARIABLE will be used to make new columns. and the values for each will come from the column called VALUE.

DONOHUE_DF <- bind_rows(dem_DONOHUE,
                        ue_rate_data,
                        poverty_rate_data,
                        crime_data,
                        population_data,
                        ps_data)
head(DONOHUE_DF)

# A tibble: 6 x 4
   YEAR STATE   VARIABLE                    VALUE
  <dbl> <chr>   <chr>                       <dbl>
1  1977 Alabama Black_Male_15_to_19_years  1.55  
2  1977 Alabama Black_Male_20_to_39_years  3.04  
3  1977 Alabama Other_Male_15_to_19_years  0.0178
4  1977 Alabama Other_Male_20_to_39_years  0.0642
5  1977 Alabama White_Male_15_to_19_years  3.58  
6  1977 Alabama White_Male_20_to_39_years 11.1

DONOHUE_DF %<>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE")

DONOHUE_DF %>%
  slice_sample(n = 10) %>%
  glimpse()

Rows: 10
Columns: 13
$ YEAR                      <dbl> 1995, 2003, 2015, 2000, 1997, 2012, 1988, 1…
$ STATE                     <chr> "California", "Massachusetts", "Oregon", "O…
$ Black_Male_15_to_19_years <dbl> 0.31138464, 0.31299021, NA, 0.37549593, 0.6…
$ Black_Male_20_to_39_years <dbl> 1.36817330, 1.05512361, NA, 1.21101852, 2.1…
$ Other_Male_15_to_19_years <dbl> 0.45350592, 0.25250036, NA, 0.68999657, 0.1…
$ Other_Male_20_to_39_years <dbl> 2.1290345, 1.1601751, NA, 1.9453069, 0.5596…
$ White_Male_15_to_19_years <dbl> 2.691623, 2.930434, NA, 2.974787, 2.889778,…
$ White_Male_20_to_39_years <dbl> 13.58846, 11.63306, NA, 10.89523, 12.14260,…
$ Unemployment_rate         <dbl> 7.9, 5.7, 5.6, 3.0, 4.8, 9.0, 6.0, 5.1, 9.3…
$ Poverty_rate              <dbl> 16.7, 10.3, 11.9, 14.9, 11.2, 22.0, 12.5, 1…
$ Viol_crime_count          <dbl> 305154, 30377, NA, 17177, 102476, 7769, 234…
$ Population                <dbl> 31493525, 6422565, NA, 3454365, 12011509, N…
$ police_per_100k_lag       <dbl> 293.3968, 316.2755, NA, 303.9922, 329.2592,…

We will also add the Right to Carry Law data using the left_join() function of the dplyr package. Which will place the DONOHUE_DF data on the left of the RTC data. Values will be matched by STATE. Then we will create a new variable called RTC_LAW using the mutate() function and the case_when() function of the dplyr package that will have the value TRUE if the current year data is equal to or greater than the year that a more premisive RTC law was adopted, otherwise the value will be FALSE.

head(RTC)

# A tibble: 6 x 2
  STATE      RTC_LAW_YEAR
  <chr>             <dbl>
1 Alabama            1975
2 Alaska             1995
3 Arizona            1995
4 Arkansas           1996
5 California          Inf
6 Colorado           2003

DONOHUE_DF %<>%
  left_join(RTC , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE))

DONOHUE_DF %>%
  slice_sample(n = 10) %>%
  glimpse()

Rows: 10
Columns: 15
$ YEAR                      <dbl> 1988, 2015, 1994, 1986, 1984, 2000, 2004, 2…
$ STATE                     <chr> "Iowa", "Maryland", "Michigan", "Alabama", …
$ Black_Male_15_to_19_years <dbl> 0.09059483, NA, 0.60273478, 1.26148927, 0.4…
$ Black_Male_20_to_39_years <dbl> 0.2938914, NA, 2.1038176, 3.6530402, 1.5079…
$ Other_Male_15_to_19_years <dbl> 0.06148022, NA, 0.08476202, 0.03605114, 0.0…
$ Other_Male_20_to_39_years <dbl> 0.2328085, NA, 0.3460699, 0.1415490, 0.1527…
$ White_Male_15_to_19_years <dbl> 3.664647, NA, 2.918864, 2.887824, 3.581117,…
$ White_Male_20_to_39_years <dbl> 14.906461, NA, 12.874229, 12.087804, 14.286…
$ Unemployment_rate         <dbl> 4.5, 5.1, 6.0, 9.7, 9.5, 4.9, 5.1, 4.7, 7.9…
$ Poverty_rate              <dbl> 9.4, 9.6, 14.1, 23.8, 13.5, 17.5, 9.3, 14.8…
$ Viol_crime_count          <dbl> 7279, NA, 72751, 22616, 41430, 13786, 29489…
$ Population                <dbl> 2768370, NA, 9584481, 3991552, 10737767, 18…
$ police_per_100k_lag       <dbl> 205.7167, NA, 272.4300, 260.5753, 216.5348,…
$ RTC_LAW_YEAR              <dbl> 2011, Inf, 2001, 1975, 2004, 2004, Inf, 199…
$ RTC_LAW                   <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FA…

Since we have differing numbers of years for each data set, we can use the drop_na() function of the tidyr package. to remove years that have incomplete data. Thus any row with NA values will be removed.

For example, we can see that for 1977, althouth we have most of the data, we do not have the poverty rate.

DONOHUE_DF %>%
  filter(YEAR == 1977) %>%
  head() %>%
  glimpse()

Rows: 6
Columns: 15
$ YEAR                      <dbl> 1977, 1977, 1977, 1977, 1977, 1977
$ STATE                     <chr> "Alabama", "Alaska", "Arizona", "Arkansas",…
$ Black_Male_15_to_19_years <dbl> 1.5457212, 0.1631338, 0.1804065, 1.0106946,…
$ Black_Male_20_to_39_years <dbl> 3.0379602, 0.9684809, 0.4796284, 1.8185525,…
$ Other_Male_15_to_19_years <dbl> 0.01781857, 1.11902724, 0.40213472, 0.03058…
$ Other_Male_20_to_39_years <dbl> 0.06421558, 2.72594532, 0.89144464, 0.09260…
$ White_Male_15_to_19_years <dbl> 3.578069, 3.828357, 4.391965, 3.879766, 4.1…
$ White_Male_20_to_39_years <dbl> 11.08537, 17.91501, 14.07430, 11.70744, 14.…
$ Unemployment_rate         <dbl> 7.3, 9.9, 8.2, 6.5, 8.3, 6.4
$ Poverty_rate              <dbl> NA, NA, NA, NA, NA, NA
$ Viol_crime_count          <dbl> 15293, 1804, 11347, 6924, 154582, 13407
$ Population                <dbl> 3782571, 397220, 2427296, 2207195, 22350332…
$ police_per_100k_lag       <dbl> 195.1054, 136.9518, 264.2447, 151.5045, 293…
$ RTC_LAW_YEAR              <dbl> 1975, 1995, 1995, 1996, Inf, 2003
$ RTC_LAW                   <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE

Another example, in 2018 we only have infromation about unemployment rates, poverty rates, and RTC laws.

DONOHUE_DF %>%
  filter(YEAR == 2018) %>%
  head() %>%
  glimpse()

Rows: 6
Columns: 15
$ YEAR                      <dbl> 2018, 2018, 2018, 2018, 2018, 2018
$ STATE                     <chr> "Alabama", "Alaska", "Arizona", "Arkansas",…
$ Black_Male_15_to_19_years <dbl> NA, NA, NA, NA, NA, NA
$ Black_Male_20_to_39_years <dbl> NA, NA, NA, NA, NA, NA
$ Other_Male_15_to_19_years <dbl> NA, NA, NA, NA, NA, NA
$ Other_Male_20_to_39_years <dbl> NA, NA, NA, NA, NA, NA
$ White_Male_15_to_19_years <dbl> NA, NA, NA, NA, NA, NA
$ White_Male_20_to_39_years <dbl> NA, NA, NA, NA, NA, NA
$ Unemployment_rate         <dbl> 3.9, 6.5, 4.7, 3.6, 4.3, 3.2
$ Poverty_rate              <dbl> 16.0, 13.1, 12.8, 15.9, 11.9, 9.1
$ Viol_crime_count          <dbl> NA, NA, NA, NA, NA, NA
$ Population                <dbl> NA, NA, NA, NA, NA, NA
$ police_per_100k_lag       <dbl> NA, NA, NA, NA, NA, NA
$ RTC_LAW_YEAR              <dbl> 1975, 1995, 1995, 1996, Inf, 2003
$ RTC_LAW                   <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE

DONOHUE_DF %<>%
 drop_na()

head(DONOHUE_DF) %>% 
  glimpse()

Rows: 6
Columns: 15
$ YEAR                      <dbl> 1980, 1980, 1980, 1980, 1980, 1980
$ STATE                     <chr> "Alabama", "Alaska", "Arizona", "Arkansas",…
$ Black_Male_15_to_19_years <dbl> 1.4567383, 0.1670456, 0.1747544, 0.9545139,…
$ Black_Male_20_to_39_years <dbl> 3.3613348, 0.9933775, 0.5267121, 1.9738213,…
$ Other_Male_15_to_19_years <dbl> 0.02128385, 1.12978156, 0.41504620, 0.03849…
$ Other_Male_20_to_39_years <dbl> 0.08608419, 2.96332905, 0.98492602, 0.12425…
$ White_Male_15_to_19_years <dbl> 3.398210, 3.627805, 4.091577, 3.740199, 3.8…
$ White_Male_20_to_39_years <dbl> 11.57164, 18.28852, 14.69238, 12.12513, 14.…
$ Unemployment_rate         <dbl> 8.9, 9.6, 6.6, 7.6, 6.8, 5.8
$ Poverty_rate              <dbl> 21.2, 9.6, 12.8, 21.5, 11.0, 8.6
$ Viol_crime_count          <dbl> 17320, 1919, 17673, 7656, 210290, 15215
$ Population                <dbl> 3899671, 404680, 2735840, 2288809, 23792840…
$ police_per_100k_lag       <dbl> 201.3247, 194.7218, 262.6616, 152.0005, 243…
$ RTC_LAW_YEAR              <dbl> 1975, 1995, 1995, 1996, Inf, 2003
$ RTC_LAW                   <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE

tail(DONOHUE_DF) %>% 
  glimpse()

Rows: 6
Columns: 15
$ YEAR                      <dbl> 2010, 2010, 2010, 2010, 2010, 2010
$ STATE                     <chr> "Vermont", "Virginia", "Washington", "West …
$ Black_Male_15_to_19_years <dbl> 0.06725669, 0.83162848, 0.15964128, 0.16343…
$ Black_Male_20_to_39_years <dbl> 0.2056042, 2.7300368, 0.6653574, 0.6164599,…
$ Other_Male_15_to_19_years <dbl> 0.1702984, 0.3274050, 0.5979246, 0.1050716,…
$ Other_Male_20_to_39_years <dbl> 0.4754297, 1.3483385, 2.1186016, 0.3126259,…
$ White_Male_15_to_19_years <dbl> 3.531216, 2.338080, 2.755299, 3.050422, 2.9…
$ White_Male_20_to_39_years <dbl> 11.363026, 9.776093, 11.195603, 11.548874, …
$ Unemployment_rate         <dbl> 6.1, 7.1, 10.0, 8.7, 8.7, 6.4
$ Poverty_rate              <dbl> 10.8, 10.7, 11.6, 16.8, 10.1, 9.6
$ Viol_crime_count          <dbl> 820, 17184, 21138, 5586, 14167, 1117
$ Population                <dbl> 625960, 8024617, 6744496, 1853973, 5691047,…
$ police_per_100k_lag       <dbl> 264.2341, 302.9677, 265.1495, 272.0644, 344…
$ RTC_LAW_YEAR              <dbl> 1970, 1995, 1961, 1990, 2012, 1995
$ RTC_LAW                   <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE

Now we have complete data and the data spans from 1980 to 2010.

DONOHUE_DF %>% distinct(YEAR) %>% pull(YEAR)

 [1] 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
[16] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
[31] 2010

If we include states that had a RTC law adopted before our time span of data, say 1975, then we only have information about crime rates and the other variables of interest after the law was adopted but not before, therefore including these states doesn’t really makes sense. Thus, we will drop the data for these states. We can use the set_diff() function of the dplyr package to see what states are in the population_data that contains all the original 51 states (recall this includes the District of Columbia) but are not in the DONOHUE_DF. The order matters here. If we did it the other way around with population_data listed second, then set_diff would test if there are any states in Donohue_DF that are not in population_data. As there are none this would result in nothing.

baseline_year <- min(DONOHUE_DF$YEAR)
censoring_year <- max(DONOHUE_DF$YEAR)

DONOHUE_DF %<>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

# DONOHUE_DF %<>% 
#   mutate(STATE = as.factor(STATE))
# 
# DONOHUE_DF %>% 
#   pull(STATE) %>% 
#   levels()

setdiff(distinct(population_data, STATE), 
        distinct(DONOHUE_DF, STATE))

# A tibble: 6 x 1
  STATE        
  <chr>        
1 Alabama      
2 Connecticut  
3 Indiana      
4 New Hampshire
5 Vermont      
6 Washington

We will also calculate a violent crime rate relative to the population in that state at that time, now that we have data for both crime count and population. Will will also calcuate the log value of this rate and the population.

DONOHUE_DF %<>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

From michael:

 DONOHUE_DF %>%
   count() %>%
   filter(n != 51) %>%
   print(n=dim(.)[1])

 summary(as.factor(DONOHUE_DF$STATE))

 max(DONOHUE_DF$YEAR) - min(DONOHUE_DF$YEAR) + 1

 DONOHUE_DF <- DONOHUE_DF %>%
   mutate(STATE = fct_collapse(STATE, "District of Columbia"=c("District of Columbia","D.C.")))

 summary(as.factor(DONOHUE_DF$STATE))
  
length(levels(DONOHUE_DF$STATE))

DONOHUE_DF <- DONOHUE_DF %>%
  group_by(STATE, YEAR) %>%
  summarise_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(DONOHUE_DF$STATE)) 

baseline_year <- min(DONOHUE_DF$YEAR)
censoring_year <- max(DONOHUE_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(DONOHUE_DF$STATE)))

length(summary(droplevels(as.factor(DONOHUE_DF$STATE))))

Lott and Mustard

We will now bind the demogrpahic data that we made for the Lott-like analysis called dem_Lott, as well as all the other datasets that we have wrangled just as we did for the Donohue-like analysis. Again, this is possible because we have the same colnames for each dataset.

LOTT_DF <- bind_rows(dem_LOTT,
                     ue_rate_data,
                     poverty_rate_data,
                     crime_data,
                     population_data,
                     ps_data) %>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE") %>%
  left_join(RTC , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE)) %>%
   drop_na()


baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

LOTT_DF %<>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

setdiff(distinct(population_data, STATE), 
        distinct(LOTT_DF, STATE))

# A tibble: 6 x 1
  STATE        
  <chr>        
1 Alabama      
2 Connecticut  
3 Indiana      
4 New Hampshire
5 Vermont      
6 Washington

LOTT_DF %<>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

Let’s see how the data compares:

We will check the dimensions of each using the base dim() function

dim(LOTT_DF)

[1] 1395   50

dim(DONOHUE_DF)

[1] 1395   20

As expected the Lott_DF is 30 columns larger, due to the 30 additional demographic variables. We can check those now as well.

LOTT_DF %>%
   colnames()

 [1] "YEAR"                           "STATE"                         
 [3] "Black_Female_10_to_19_years"    "Black_Female_20_to_29_years"   
 [5] "Black_Female_30_to_39_years"    "Black_Female_40_to_49_years"   
 [7] "Black_Female_50_to_64_years"    "Black_Female_65_years_and_over"
 [9] "Black_Male_10_to_19_years"      "Black_Male_20_to_29_years"     
[11] "Black_Male_30_to_39_years"      "Black_Male_40_to_49_years"     
[13] "Black_Male_50_to_64_years"      "Black_Male_65_years_and_over"  
[15] "Other_Female_10_to_19_years"    "Other_Female_20_to_29_years"   
[17] "Other_Female_30_to_39_years"    "Other_Female_40_to_49_years"   
[19] "Other_Female_50_to_64_years"    "Other_Female_65_years_and_over"
[21] "Other_Male_10_to_19_years"      "Other_Male_20_to_29_years"     
[23] "Other_Male_30_to_39_years"      "Other_Male_40_to_49_years"     
[25] "Other_Male_50_to_64_years"      "Other_Male_65_years_and_over"  
[27] "White_Female_10_to_19_years"    "White_Female_20_to_29_years"   
[29] "White_Female_30_to_39_years"    "White_Female_40_to_49_years"   
[31] "White_Female_50_to_64_years"    "White_Female_65_years_and_over"
[33] "White_Male_10_to_19_years"      "White_Male_20_to_29_years"     
[35] "White_Male_30_to_39_years"      "White_Male_40_to_49_years"     
[37] "White_Male_50_to_64_years"      "White_Male_65_years_and_over"  
[39] "Unemployment_rate"              "Poverty_rate"                  
[41] "Viol_crime_count"               "Population"                    
[43] "police_per_100k_lag"            "RTC_LAW_YEAR"                  
[45] "RTC_LAW"                        "TIME_0"                        
[47] "TIME_INF"                       "Viol_crime_rate_1k"            
[49] "Viol_crime_rate_1k_log"         "Population_log"

DONOHUE_DF %>%
   colnames()

 [1] "YEAR"                      "STATE"                    
 [3] "Black_Male_15_to_19_years" "Black_Male_20_to_39_years"
 [5] "Other_Male_15_to_19_years" "Other_Male_20_to_39_years"
 [7] "White_Male_15_to_19_years" "White_Male_20_to_39_years"
 [9] "Unemployment_rate"         "Poverty_rate"             
[11] "Viol_crime_count"          "Population"               
[13] "police_per_100k_lag"       "RTC_LAW_YEAR"             
[15] "RTC_LAW"                   "TIME_0"                   
[17] "TIME_INF"                  "Viol_crime_rate_1k"       
[19] "Viol_crime_rate_1k_log"    "Population_log"

Lastly, we will check that the YEAR values are the same. We can use the setequal() function of the dplyr package to see if the values are the same.

setequal(DONOHUE_DF %>% distinct(YEAR),
          LOTT_DF %>% distinct(YEAR))

[1] TRUE

Looks as expected! we have

from Michael:

LOTT_DF %>%
  group_by(YEAR) %>%
  tally() %>%
  filter(n != 51) %>%
  print(n=dim(.)[1])

summary(as.factor(LOTT_DF$STATE))

max(LOTT_DF$YEAR) - min(LOTT_DF$YEAR) + 1

LOTT_DF <- LOTT_DF %>%
  mutate(STATE = fct_collapse(STATE, "District of Columbia" = c("District of Columbia","D.C.")))

summary(as.factor(LOTT_DF$STATE))
  
length(levels(LOTT_DF$STATE))

LOTT_DF <- LOTT_DF %>%
  group_by(STATE, YEAR) %>%
  summarise_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(LOTT_DF$STATE)) 

baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

LOTT_DF <- LOTT_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

LOTT_DF <- LOTT_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(LOTT_DF$STATE)))

length(summary(droplevels(as.factor(LOTT_DF$STATE))))

Data Exploration

Let’s do some quick visualizations to get a sense of our outcome of interest, the violent crime data.

First we will plot the rate of violent crime over time to get a sense of the general trend.

To do so we need to summarise the data for each year across all of the states. Thus we will use the group_by() function and the summarise() functions to calculate an overall sum of violent crime relative to the population for each year.

Then we will use the ggplot2 package to plot the data. The first step in creating a plot with this package is to use the ggplot() function and the aes() argument to specify what data should be plotted on the x-axis and what data should be plotted in on the y-axis. Then we select what type of plot we would like to make using one of the geom_*() functions. Please see this case study for more details.

We can use the scale_x_continuous() and scale_y_continuous() functions to modify the axis labels.

The labs() function can be used to add labels to the plot, while the theme() function allows for manipulation of the detials of the labels, like size and angle.

All of these functions are part of the ggplot2 package.

 DONOHUE_DF %>%
  group_by(YEAR) %>%
  summarise(Viol_crime_count = sum(Viol_crime_count),
                  Population = sum(Population),
                    .groups = "drop") %>%
  mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
  ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log)) +
  geom_line() +
  scale_x_continuous(breaks = seq(1980, 2010, by = 1),
                     limits = c(1980, 2010),
                     labels = c(seq(1980, 2010, by = 1))) +
  scale_y_continuous(breaks = seq(5.75, 6.75, by = 0.25),
                     limits = c(5.75, 6.75)) +
  labs(title = "Crime rates fluctuate over time",
       x = "Year",
       y = "ln(violent crimes per 100,000 people)") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 90))

Interesting! It appears that there was an overall national peak in violent crime in the early 1990s that has since then declined.

Now let’s take a look at each state.

We will use the ggrepel package to add text to the plot using the geom_text_repel() function. This is especially useful when there is alot of text, as this function reduces the overlap of text labels. Again see this case study for more details on how to add labels to elements of plots.

DONOHUE_DF %>%
  mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
  ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log, color = STATE)) +
  geom_point(size = 0.5) +
  geom_line(aes(group=STATE),
            size = 0.5,
            show.legend = FALSE) +
  geom_text_repel(data = DONOHUE_DF %>%
              mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
              filter(YEAR == last(YEAR)),
            aes(label = STATE,
                x = YEAR,
                y = Viol_crime_rate_100k_log),
            size = 3,
            alpha = 1,
            nudge_x = 10,
            direction = "y",
            hjust = 1,
            vjust = 1,
            segment.size = 0.25,
            segment.alpha = 0.25,
            force = 1,
            max.iter = 9999) +
  guides(color = FALSE) +
  scale_x_continuous(breaks = seq(1980, 2015, by = 1),
                     limits = c(1980, 2015),
                     labels = c(seq(1980, 2010, by = 1), rep("", 5))) +
  scale_y_continuous(breaks = seq(3.5, 8.5, by = 0.5),
                     limits = c(3.5, 8.5)) +
  labs(title = "States have different levels of crime",
       x = "Year",
       y = "ln(violent crimes per 100,000 people)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))

Looks like the crime rages vary quite a bit from one state to another. Some states show increased crime over time while others show decreased crime.

Now let’s take a closer look at some of our other variables.

We can also use the vis_miss() function of the naniar package to confirm that there are no missing values.

DONOHUE_DF %>%
  naniar::vis_miss()

Looks like no missing data!

LOTT_DF %>%
  naniar::vis_miss()

Same for the LOTT_DF.

We can use the skim() of the skimr package to get a better sense of the data. This also shows missingingness, as well as standard deviations, spread, and means for our data. Also notice that there is a histogram of each variable.

skimr::skim(DONOHUE_DF)

Data summary
Name	DONOHUE_DF
Number of rows	1395
Number of columns	20
_______________________
Column type frequency:
character	1
logical	1
numeric	18
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
STATE	0	1	4	20	0	45	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
RTC_LAW	0	1	0.37	FAL: 883, TRU: 512

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
YEAR	1	1995.00	8.95	1980.00	1987.00	1995.00	2003.00	2010.00	▇▇▇▇▇
Black_Male_15_to_19_years	1	0.52	0.51	0.02	0.13	0.35	0.73	3.46	▇▂▁▁▁
Black_Male_20_to_39_years	1	1.73	1.76	0.07	0.50	1.17	2.30	11.33	▇▂▁▁▁
Other_Male_15_to_19_years	1	0.26	0.40	0.01	0.08	0.14	0.28	2.90	▇▁▁▁▁
Other_Male_20_to_39_years	1	0.92	1.40	0.07	0.31	0.55	0.99	9.90	▇▁▁▁▁
White_Male_15_to_19_years	1	3.09	0.72	0.55	2.69	3.14	3.54	4.99	▁▁▇▇▁
White_Male_20_to_39_years	1	12.55	2.29	4.41	11.18	12.65	14.15	18.44	▁▂▇▇▂
Unemployment_rate	1	6.03	2.10	2.30	4.50	5.60	7.20	17.80	▇▇▂▁▁
Poverty_rate	1	13.34	3.84	5.70	10.40	12.70	15.50	27.20	▃▇▅▁▁
Viol_crime_count	1	31760.77	46494.46	322.00	5107.50	14412.00	38782.50	345624.00	▇▁▁▁▁
Population	1	5446810.81	6070687.33	404680.00	1363737.50	3504892.00	6411701.00	37349363.00	▇▂▁▁▁
police_per_100k_lag	1	316.57	115.59	83.76	248.53	301.00	358.31	1021.14	▅▇▁▁▁
RTC_LAW_YEAR	1	Inf	NaN	1985.00	1995.00	1997.00	2011.00	Inf	▇▇▂▅▂
TIME_0	1	1980.00	0.00	1980.00	1980.00	1980.00	1980.00	1980.00	▁▁▇▁▁
TIME_INF	1	2010.00	0.00	2010.00	2010.00	2010.00	2010.00	2010.00	▁▁▇▁▁
Viol_crime_rate_1k	1	5.05	3.19	0.48	2.85	4.54	6.44	29.30	▇▃▁▁▁
Viol_crime_rate_1k_log	1	1.45	0.60	-0.74	1.05	1.51	1.86	3.38	▁▂▇▅▁
Population_log	1	14.99	1.05	12.91	14.13	15.07	15.67	17.44	▃▅▇▅▂

skimr::skim(LOTT_DF)

Data summary
Name	LOTT_DF
Number of rows	1395
Number of columns	50
_______________________
Column type frequency:
character	1
logical	1
numeric	48
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
STATE	0	1	4	20	0	45	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
RTC_LAW	0	1	0.37	FAL: 883, TRU: 512

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
YEAR	1	1995.00	8.95	1980.00	1987.00	1995.00	2003.00	2010.00	▇▇▇▇▇
Black_Female_10_to_19_years	1	1.00	1.02	0.02	0.23	0.63	1.44	6.53	▇▂▁▁▁
Black_Female_20_to_29_years	1	0.99	1.09	0.02	0.23	0.60	1.36	7.73	▇▂▁▁▁
Black_Female_30_to_39_years	1	0.91	1.00	0.01	0.19	0.57	1.27	6.11	▇▂▁▁▁
Black_Female_40_to_49_years	1	0.75	0.86	0.01	0.13	0.48	1.09	5.45	▇▂▁▁▁
Black_Female_50_to_64_years	1	0.76	0.96	0.00	0.12	0.44	1.06	6.10	▇▂▁▁▁
Black_Female_65_years_and_over	1	0.60	0.85	0.00	0.07	0.34	0.81	6.12	▇▁▁▁▁
Black_Male_10_to_19_years	1	1.02	1.01	0.03	0.26	0.67	1.46	6.32	▇▂▁▁▁
Black_Male_20_to_29_years	1	0.93	0.93	0.04	0.29	0.65	1.24	6.57	▇▂▁▁▁
Black_Male_30_to_39_years	1	0.81	0.84	0.02	0.23	0.54	1.08	5.37	▇▂▁▁▁
Black_Male_40_to_49_years	1	0.65	0.72	0.01	0.15	0.43	0.92	4.45	▇▂▁▁▁
Black_Male_50_to_64_years	1	0.62	0.76	0.00	0.13	0.38	0.86	4.79	▇▂▁▁▁
Black_Male_65_years_and_over	1	0.38	0.51	0.00	0.05	0.24	0.51	3.56	▇▁▁▁▁
Other_Female_10_to_19_years	1	0.51	0.77	0.03	0.15	0.27	0.55	5.33	▇▁▁▁▁
Other_Female_20_to_29_years	1	0.49	0.70	0.04	0.17	0.30	0.55	5.55	▇▁▁▁▁
Other_Female_30_to_39_years	1	0.47	0.74	0.04	0.16	0.28	0.51	5.36	▇▁▁▁▁
Other_Female_40_to_49_years	1	0.38	0.69	0.02	0.11	0.21	0.38	5.46	▇▁▁▁▁
Other_Female_50_to_64_years	1	0.38	0.83	0.02	0.09	0.18	0.35	7.10	▇▁▁▁▁
Other_Female_65_years_and_over	1	0.24	0.72	0.01	0.05	0.09	0.18	6.20	▇▁▁▁▁
Other_Male_10_to_19_years	1	0.52	0.80	0.03	0.15	0.28	0.56	5.58	▇▁▁▁▁
Other_Male_20_to_29_years	1	0.48	0.70	0.03	0.17	0.29	0.53	5.33	▇▁▁▁▁
Other_Male_30_to_39_years	1	0.44	0.71	0.03	0.14	0.26	0.47	5.06	▇▁▁▁▁
Other_Male_40_to_49_years	1	0.35	0.65	0.02	0.09	0.19	0.34	5.13	▇▁▁▁▁
Other_Male_50_to_64_years	1	0.33	0.73	0.01	0.08	0.15	0.29	6.50	▇▁▁▁▁
Other_Male_65_years_and_over	1	0.19	0.58	0.01	0.03	0.07	0.14	4.51	▇▁▁▁▁
White_Female_10_to_19_years	1	5.72	1.38	0.94	5.01	5.82	6.61	9.45	▁▁▇▆▁
White_Female_20_to_29_years	1	6.09	1.36	1.59	5.23	5.92	6.93	9.66	▁▂▇▅▂
White_Female_30_to_39_years	1	6.17	1.22	1.53	5.46	6.28	7.02	8.95	▁▁▅▇▂
White_Female_40_to_49_years	1	5.58	1.23	1.20	4.85	5.68	6.41	8.33	▁▁▇▇▃
White_Female_50_to_64_years	1	6.56	1.45	1.72	6.01	6.57	7.34	11.40	▁▂▇▂▁
White_Female_65_years_and_over	1	6.38	1.70	1.05	5.37	6.62	7.51	9.90	▁▁▆▇▂
White_Male_10_to_19_years	1	6.04	1.43	1.02	5.30	6.14	6.95	9.74	▁▁▇▇▁
White_Male_20_to_29_years	1	6.28	1.33	2.41	5.43	6.12	7.14	10.84	▁▆▇▃▁
White_Male_30_to_39_years	1	6.27	1.19	1.93	5.59	6.33	7.06	9.67	▁▂▇▆▁
White_Male_40_to_49_years	1	5.59	1.22	1.35	4.79	5.67	6.43	8.39	▁▁▇▇▂
White_Male_50_to_64_years	1	6.26	1.40	1.78	5.64	6.19	6.94	10.93	▁▂▇▂▁
White_Male_65_years_and_over	1	4.56	1.19	1.02	3.80	4.78	5.34	7.51	▁▂▇▇▁
Unemployment_rate	1	6.03	2.10	2.30	4.50	5.60	7.20	17.80	▇▇▂▁▁
Poverty_rate	1	13.34	3.84	5.70	10.40	12.70	15.50	27.20	▃▇▅▁▁
Viol_crime_count	1	31760.77	46494.46	322.00	5107.50	14412.00	38782.50	345624.00	▇▁▁▁▁
Population	1	5446810.81	6070687.33	404680.00	1363737.50	3504892.00	6411701.00	37349363.00	▇▂▁▁▁
police_per_100k_lag	1	316.57	115.59	83.76	248.53	301.00	358.31	1021.14	▅▇▁▁▁
RTC_LAW_YEAR	1	Inf	NaN	1985.00	1995.00	1997.00	2011.00	Inf	▇▇▂▅▂
TIME_0	1	1980.00	0.00	1980.00	1980.00	1980.00	1980.00	1980.00	▁▁▇▁▁
TIME_INF	1	2010.00	0.00	2010.00	2010.00	2010.00	2010.00	2010.00	▁▁▇▁▁
Viol_crime_rate_1k	1	5.05	3.19	0.48	2.85	4.54	6.44	29.30	▇▃▁▁▁
Viol_crime_rate_1k_log	1	1.45	0.60	-0.74	1.05	1.51	1.86	3.38	▁▂▇▅▁
Population_log	1	14.99	1.05	12.91	14.13	15.07	15.67	17.44	▃▅▇▅▂

Data Analysis

Donohue, et al.

OK! We are now ready to start analyzing our data!.

We have what is called panel data . This is a special type of longitundinal data. Longitudinal data are data measurments taken over time. Panel data are data repeatidly measured for for multiple panel members or individuals over time. This is in contrast with time series data, which measures one individual over time and cross sectional data, which measures multiple individuals at one point in time. In otherwords, panel data is a combination of both, in this case we measure multiple individuals over multiple time periods. In our case, we have measurements of violent crime and other variables for each state over many years. Therefore we are using measurements about the same states over time.

In a panel analysis there are \(N\) individual panel members and \(T\) time points.

There are two types of panels:
1. Balanced - At each time point (\(T\)), there are data points for each individual(\(N\)).

Time Points (\(T\))	Individuals (\(N\))
1977	Navada
1977	Alabama
1977	Kansas
1978	Nevada
1978	Alabama
1978	Kansas
1979	Nevada
1979	Alabama
1979	Kansas

Unbalanced - There may be data points missing for some inidividuals (\(N\)) at some time points (\(T\)).

Time Points (\(T\))	Individuals (\(N\))
1977	Navada
1977	Alabama
1978	Nevada
1978	Alabama
1979	Nevada
1979	Alabama
1979	Kansas

Overall in a a balanced panel, we have \(n\) observations, where \(n = N*T\).

In an unbalanced panel, the number of observations is less than \(N*T\).

In our case we have:
\(N\) = 45 states (recall that we removed those who had adopted a RTC law before 1980)
\(T\) = 31 years (1980 - 2010)

In every year we have measurements for each state (as we just saw above), thus our panel is balanced.

So, our total observations \(n = 45*31\), thus \(n\) = 1395.

We will be performing a panel linear regression model analysis.

In such an analysis we will model our data according to this generic model:

\[Y_{it} = a + B'X_{it} + u_{it},\\ u_{it}=\mu _{i}+v_{it}\] Where \(i\) is the individual dimension (in our case individual states) and \(t\) is the time dimension.

Some independent variables or regressors \(X_{it}\) will vary across individuals and time, while others will be fixed across the time of the study (or don’t change over time), while others still will be fixed across individuals but vary across time periods.

\(\mu _{i}\) are individual effects that are time independent (aka. fixed over time).

\(v_{it}\) are random effects that vary with time

There are three general subtypes of panel regression analysis.

Overall, they assume that the different individuals are independent, however the same data for the same individual may be correlated across time.

The main difference between the three subtypes are the assumptions about unobserved differences between individuals.

independently pooled panels - assumes that there are no individual effects that are independent of time and also no effect of time on all the individuals. In otherwords, the independent variables are not correlated with any error term. This is essentially an ordinary least squares linear regression. This type of panel regression makes the most assumptions and is therefore typically not used for panel data.
fixed effects - assumes that there are unknown or unobserved unique aspects about the individuals or heterogeneity among indivudals \(a_i\) that are not explained by the independent variables but influence the outcome variable of interest. They do not vary with time or in otherwords are fixed over time but may be correlated with independent variables \(X_{it}\).

In this case the intercept can be different for each indivudual \({a_i}\), but the slope is the same across all the individuals.

These individual \(a_i\) effects can be correlated with the independent variables \(X\).

\[Y_{it} = a_i + B'X_{it} + u_{it},\\ u_{it}=\mu _{i}+v_{it}\] This type of panel regression makes the least assumptions.

random effects - assumes that there are unknown or unobserved unique qualities about the individuals that influences the outcome variable of interest that are not correlated with the independent variables.

Thus, the random effects model actually makes more assumptions than the fixed effect model.

\[Y_{it} = B'X_{it} + (a_i + e_{it})}\] So each indivual has the sampe slope and the same overall error term (a_i + e_{it}).

See here and here for more information about these different models.

We will be performing a fixed effect panel regression analysis, as we do in fact think that some of the unobserved qualities about the different states that may be correlated with some of our independent variables. For example, the level of economic opportunity might be an unobserved feature about the states that influences violent crime rate and would be possibly correlated with poverty rate and unemployment.

avocado:We assume that data for the same individual may be correlated, but we assume that the different indivudals are independent.

avocado from Michael:Some code taken from http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

To perform our analysis we will be using the plm package. This stands for Panel Linear Model.

We need to use a special type of data to use this package, called a pdata.frame which is short for panel data frame. This allows us to specify that we are using panel data and what the panel structure looks like.

We need to indicate variable should be used to identify the individuals in our panel, and what variable should be used to identify the time periods in our panel. In our case the STATE variable identifies the individuals and the YEAR variable identifies the time periods.

We can specify this structure using the pdata.frame() function of the plm package, by using the index argument, where the individual variale is specified first followed by the time variable, like so: `index=c(“Individual_Variable_NAME”, “Time_Period_Variable_NAME”).

d_panel_DONOHUE <- pdata.frame(DONOHUE_DF, index=c("STATE", "YEAR"))

class(d_panel_DONOHUE)

[1] "pdata.frame" "data.frame"

slice_head(d_panel_DONOHUE, n = 3)

            YEAR  STATE Black_Male_15_to_19_years Black_Male_20_to_39_years
Alaska-1980 1980 Alaska                 0.1670456                 0.9933775
Alaska-1981 1981 Alaska                 0.1732299                 1.0028219
Alaska-1982 1982 Alaska                 0.1737069                 1.0204445
            Other_Male_15_to_19_years Other_Male_20_to_39_years
Alaska-1980                  1.129782                  2.963329
Alaska-1981                  1.124441                  2.974775
Alaska-1982                  1.069821                  3.015071
            White_Male_15_to_19_years White_Male_20_to_39_years
Alaska-1980                  3.627805                  18.28852
Alaska-1981                  3.558261                  18.12821
Alaska-1982                  3.391844                  18.10666
            Unemployment_rate Poverty_rate Viol_crime_count Population
Alaska-1980               9.6          9.6             1919     404680
Alaska-1981               9.4          9.0             2537     418519
Alaska-1982               9.9         10.6             2732     449608
            police_per_100k_lag RTC_LAW_YEAR RTC_LAW TIME_0 TIME_INF
Alaska-1980            194.7218         1995   FALSE   1980     2010
Alaska-1981            200.2299         1995   FALSE   1980     2010
Alaska-1982            191.0553         1995   FALSE   1980     2010
            Viol_crime_rate_1k Viol_crime_rate_1k_log Population_log
Alaska-1980           4.742018               1.556463       12.91085
Alaska-1981           6.061851               1.802015       12.94448
Alaska-1982           6.076404               1.804413       13.01613

Indeed we have now created a pdata.frame object and we can see that the row names show the individual states and time period years.

OK, now we are ready to run our panel linear model on our panel data frame.

To do so we will use the plm() function and we will specify the formula for our model, where the dependent variable Viol_crime_rate_1k_log will be on the left of our ~ sign and all of the independent variables will be listed on the right with + signs in between each.

We also need to specify what type of effect we would like to model and what type of model we would like to use.

There are three main options for the effect argument: 1) individual - model for the effect of indivudal identity 2) time - model for the effect of time 3) twoways - meaning modeling for the effect of both individual identity and time

There are four main options for the model argument:
1) pooling - standard pooled ordinary least squares regression model
2) within - fixed effects model (variation between individuals is ignored, model compares individuals to themselves at different periods of time)
3) between - fixed effects model (variation within individuals from one time point to another is ignored, model compares different individuals at each point of time)
4) random - random effects (each state has a different intercept but force it to follow a normal distribution - requires more assumptions)

Typically it is best to think about what you are trying to evaluate with your data in trying to choose how to model your data. However, there are also some tests that can help to assess this which we will briefly cover.

We are interested in how violence in each state varied over time, thus we are interested in within STATEvariation, so we will perform our plm with the model = within argument to perform this particular type of fixed effects model.

We also speculate that there is an effect of individual STATE identity and time on violent crime rate. In otherwords, we expect some states to have high rates of crime, and others to have low rates of crime. We also expect crime to change over time.

If we were to perform this type of analysis we would use the effect = "twoways" argument in our plm() function like so:

DONOHUE_OUTPUT <- plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        effect = "twoways",
                        model = "within",
                        data = d_panel_DONOHUE)

To see the results we can use the base summary() function. We can view this output in tidy format using the tidy() function of the broom package.

avocado I think the analysis variable is a lable for plots

summary(DONOHUE_OUTPUT)

Twoways effects Within Model

Call:
plm(formula = Viol_crime_rate_1k_log ~ RTC_LAW + White_Male_15_to_19_years + 
    White_Male_20_to_39_years + Black_Male_15_to_19_years + Black_Male_20_to_39_years + 
    Other_Male_15_to_19_years + Other_Male_20_to_39_years + Unemployment_rate + 
    Poverty_rate + Population_log + police_per_100k_lag, data = d_panel_DONOHUE, 
    effect = "twoways", model = "within")

Balanced Panel: n = 45, T = 31, N = 1395

Residuals:
       Min.     1st Qu.      Median     3rd Qu.        Max. 
-0.57957437 -0.08942194 -0.00090654  0.08673054  1.11216999 

Coefficients:
                             Estimate  Std. Error t-value  Pr(>|t|)    
RTC_LAWTRUE                0.01796779  0.01663911  1.0799 0.2804066    
White_Male_15_to_19_years -0.00091825  0.02724210 -0.0337 0.9731160    
White_Male_20_to_39_years  0.03466473  0.00972839  3.5633 0.0003794 ***
Black_Male_15_to_19_years -0.05673593  0.05746052 -0.9874 0.3236341    
Black_Male_20_to_39_years  0.12605439  0.01931450  6.5264 9.607e-11 ***
Other_Male_15_to_19_years  0.69201638  0.11322394  6.1119 1.297e-09 ***
Other_Male_20_to_39_years -0.30276797  0.03811855 -7.9428 4.226e-15 ***
Unemployment_rate         -0.01685806  0.00489952 -3.4408 0.0005984 ***
Poverty_rate              -0.00780235  0.00295720 -2.6384 0.0084280 ** 
Population_log            -0.17991653  0.06041773 -2.9779 0.0029559 ** 
police_per_100k_lag        0.00060391  0.00013689  4.4115 1.111e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    43.211
Residual Sum of Squares: 36.716
R-Squared:      0.15031
Adj. R-Squared: 0.095138
F-statistic: 21.0514 on 11 and 1309 DF, p-value: < 2.22e-16

DONOHUE_OUTPUT_TIDY <- tidy(DONOHUE_OUTPUT, conf.int = 0.95)

DONOHUE_OUTPUT_TIDY

# A tibble: 11 x 7
   term                 estimate std.error statistic  p.value conf.low conf.high
   <chr>                   <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
 1 RTC_LAWTRUE          0.0180    0.0166      1.08   2.80e- 1 -1.46e-2  0.0506  
 2 White_Male_15_to_1… -0.000918  0.0272     -0.0337 9.73e- 1 -5.43e-2  0.0525  
 3 White_Male_20_to_3…  0.0347    0.00973     3.56   3.79e- 4  1.56e-2  0.0537  
 4 Black_Male_15_to_1… -0.0567    0.0575     -0.987  3.24e- 1 -1.69e-1  0.0559  
 5 Black_Male_20_to_3…  0.126     0.0193      6.53   9.61e-11  8.82e-2  0.164   
 6 Other_Male_15_to_1…  0.692     0.113       6.11   1.30e- 9  4.70e-1  0.914   
 7 Other_Male_20_to_3… -0.303     0.0381     -7.94   4.23e-15 -3.77e-1 -0.228   
 8 Unemployment_rate   -0.0169    0.00490    -3.44   5.98e- 4 -2.65e-2 -0.00726 
 9 Poverty_rate        -0.00780   0.00296    -2.64   8.43e- 3 -1.36e-2 -0.00201 
10 Population_log      -0.180     0.0604     -2.98   2.96e- 3 -2.98e-1 -0.0615  
11 police_per_100k_lag  0.000604  0.000137    4.41   1.11e- 5  3.36e-4  0.000872

DONOHUE_OUTPUT_TIDY$Analysis <- "Analysis 1"

We will now perform a test to determine if we could have simply used a pooled model. This test evaluates if the coefficients (including the intercepts) are equal. To perform this test we will use the pooltest() function of the plm package to compare the pooled model to the fixed effect within model.

pooltest(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag, 
         data = d_panel_DONOHUE, 
         model = "within")


    F statistic

data:  Viol_crime_rate_1k_log ~ RTC_LAW + White_Male_15_to_19_years +  ...
F = 15.299, df1 = 484, df2 = 855, p-value < 2.2e-16
alternative hypothesis: unstability

The p-value is less than a significance threshold of .05, thus we reject the null that our coeffients are all equal. Thus the within fixed effects model fit the data better.

We can also perform a test to evaluate if there is indeed an individual effect and a time effect in our model.

We can use the plmtest() function of the plm package. This performs a Lagrange Multiplier Test. To do so, we need to get the output for a simple pooled model.

DONOHUE_pool_model <-plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        model = "pooling",
                        data=d_panel_DONOHUE)

plmtest(DONOHUE_pool_model, effect = c("twoways"))


    Lagrange Multiplier Test - two-ways effects (Honda) for balanced
    panels

data:  Viol_crime_rate_1k_log ~ RTC_LAW + White_Male_15_to_19_years +  ...
normal = 72.56, p-value < 2.2e-16
alternative hypothesis: significant effects

Again, the p-value is much smaller than the significance threshold of < 0.05. Therefore we reject the null that there are no effects, and we can feel confident with proceeding with a twoway effect model.

There is one more test that we could perform. To test if using a random effect model would be more appropriate compared to the fixed effect model, one could use the Hausmen test for this (also called the Durbin-Wu-Hausman test). This can be implemented using the phtest() function of the plm package.

DONOHUE_random_model <-  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                      effect = "twoways",
                      model = "random",
                      data=d_panel_DONOHUE)

DONOHUE_within_model <-  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                       effect = "twoways",
                       model = "within",
                       data=d_panel_DONOHUE)

phtest(DONOHUE_within_model, DONOHUE_random_model)


    Hausman Test

data:  Viol_crime_rate_1k_log ~ RTC_LAW + White_Male_15_to_19_years +  ...
chisq = 59.54, df = 11, p-value = 1.129e-08
alternative hypothesis: one model is inconsistent

This test evaluates if there are errors \(u_i\) that are correlated with any of the independent variables.

We reject the null hypothesis, that there are no inconsistencies, and are confirmed in our plan to used a fixed effects model.

For more information on these tests and this package, see here and here.

avocado- stephanie- what do you think about referencing these slides from Princeton?

A final note about the fixed and random effects terminology and how this is slightly different than other definitions:

According to the documentation for the PLM package: >The fixed/random effects terminology in econometrics is often recognized to be misleading, as both are treated as random variates in modern econometrics (see, e.g., Wooldridge (2002) 10.2.1). It has been recognized since Mundlak’s classic paper (Mundlak (1978)) that the fundamental issue is whether the unobserved effects are correlated with the regressors or not. In this last case, they can safely be left in the error term, and the serial correlation they induce is cared for by means of appropriate GLS transformations. On the contrary, in the case of correlation, “fixed effects” methods such as least squares dummy variables or time-demeaning are needed, which explicitly, although inconsistently27, estimate a group– (or time–) invariant additional parameter for each group (or time period).

Thus, from the point of view of model specification, having fixed effects in an econometric model has the meaning of allowing the intercept to vary with group, or time, or both, while the other parameters are generally still assumed to be homogeneous. Having random effects means having a group– (or time–, or both) specific component in the error term.

In the mixed models literature, on the contrary, fixed effect indicates a parameter that is assumed constant, while random effects are parameters that vary randomly around zero according to a joint multivariate normal distribution.

Lott and Mustard

Ok, now we will do the same for the Lott-like analysis. In this case we would have a very large formula to write. So instead, we can use the as.formula() function of the stats package and the base paste() function to combine all of our explanatory variables into one formula without making a mistake. First we will create an object where we select only for the explanatory variables.

avocado I tried using glue or dplyr::collapse to make this tidyverse but this didnt work

LOTT_variables <- LOTT_DF %>%
  dplyr::select(RTC_LAW,
                contains(c("White","Black","Other")),
                Unemployment_rate,
                Poverty_rate,
                Population_log,
                police_per_100k_lag) %>%
  colnames()


LOTT_fmla <- as.formula(paste("Viol_crime_rate_1k_log ~",
                              paste(LOTT_variables, collapse = " + ")
                              )
                        )

LOTT_fmla

Viol_crime_rate_1k_log ~ RTC_LAW + White_Female_10_to_19_years + 
    White_Female_20_to_29_years + White_Female_30_to_39_years + 
    White_Female_40_to_49_years + White_Female_50_to_64_years + 
    White_Female_65_years_and_over + White_Male_10_to_19_years + 
    White_Male_20_to_29_years + White_Male_30_to_39_years + White_Male_40_to_49_years + 
    White_Male_50_to_64_years + White_Male_65_years_and_over + 
    Black_Female_10_to_19_years + Black_Female_20_to_29_years + 
    Black_Female_30_to_39_years + Black_Female_40_to_49_years + 
    Black_Female_50_to_64_years + Black_Female_65_years_and_over + 
    Black_Male_10_to_19_years + Black_Male_20_to_29_years + Black_Male_30_to_39_years + 
    Black_Male_40_to_49_years + Black_Male_50_to_64_years + Black_Male_65_years_and_over + 
    Other_Female_10_to_19_years + Other_Female_20_to_29_years + 
    Other_Female_30_to_39_years + Other_Female_40_to_49_years + 
    Other_Female_50_to_64_years + Other_Female_65_years_and_over + 
    Other_Male_10_to_19_years + Other_Male_20_to_29_years + Other_Male_30_to_39_years + 
    Other_Male_40_to_49_years + Other_Male_50_to_64_years + Other_Male_65_years_and_over + 
    Unemployment_rate + Poverty_rate + Population_log + police_per_100k_lag

That is quite the formula!

Ok, now again we will make a panel data frame and we will perform fit a fixed effect two-way model for time and individuals (STATE) with this data as well.

d_panel_LOTT <- pdata.frame(LOTT_DF, index=c("STATE", "YEAR"))

LOTT_OUTPUT <- plm(LOTT_fmla,
                      model = "within",
                     effect = "twoways",
                       data = d_panel_LOTT)

summary(LOTT_OUTPUT)

Twoways effects Within Model

Call:
plm(formula = LOTT_fmla, data = d_panel_LOTT, effect = "twoways", 
    model = "within")

Balanced Panel: n = 45, T = 31, N = 1395

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-0.5448906 -0.0780395  0.0026738  0.0788052  0.5847263 

Coefficients:
                                  Estimate  Std. Error  t-value  Pr(>|t|)    
RTC_LAWTRUE                    -0.04687169  0.01641851  -2.8548 0.0043758 ** 
White_Female_10_to_19_years     0.62441376  0.15103427   4.1343 3.793e-05 ***
White_Female_20_to_29_years    -0.05942541  0.06332108  -0.9385 0.3481763    
White_Female_30_to_39_years     0.16028113  0.08045953   1.9921 0.0465755 *  
White_Female_40_to_49_years     0.10087510  0.08170707   1.2346 0.2172082    
White_Female_50_to_64_years    -0.37624966  0.06303172  -5.9692 3.083e-09 ***
White_Female_65_years_and_over  0.20636690  0.04742430   4.3515 1.460e-05 ***
White_Male_10_to_19_years      -0.59141591  0.14436974  -4.0965 4.457e-05 ***
White_Male_20_to_29_years       0.08717546  0.05862342   1.4870 0.1372503    
White_Male_30_to_39_years      -0.12514225  0.08588569  -1.4571 0.1453400    
White_Male_40_to_49_years      -0.21812366  0.07293615  -2.9906 0.0028375 ** 
White_Male_50_to_64_years       0.37845575  0.07314122   5.1743 2.653e-07 ***
White_Male_65_years_and_over   -0.20915907  0.06659815  -3.1406 0.0017246 ** 
Black_Female_10_to_19_years    -1.03146594  0.43610403  -2.3652 0.0181697 *  
Black_Female_20_to_29_years    -0.02721685  0.17462559  -0.1559 0.8761693    
Black_Female_30_to_39_years    -0.03246043  0.20498789  -0.1584 0.8742037    
Black_Female_40_to_49_years     0.43820099  0.23524130   1.8628 0.0627234 .  
Black_Female_50_to_64_years     0.04906111  0.21393128   0.2293 0.8186482    
Black_Female_65_years_and_over  0.07226074  0.24373031   0.2965 0.7669130    
Black_Male_10_to_19_years       1.22536162  0.44559642   2.7499 0.0060447 ** 
Black_Male_20_to_29_years      -0.06587312  0.18392655  -0.3581 0.7202909    
Black_Male_30_to_39_years       0.24720746  0.23673862   1.0442 0.2965804    
Black_Male_40_to_49_years      -0.66869983  0.27173041  -2.4609 0.0139904 *  
Black_Male_50_to_64_years      -0.16737616  0.23977741  -0.6980 0.4852740    
Black_Male_65_years_and_over   -0.58743446  0.34691532  -1.6933 0.0906404 .  
Other_Female_10_to_19_years     0.70957924  0.49539878   1.4323 0.1522910    
Other_Female_20_to_29_years    -1.16489945  0.26997487  -4.3148 1.720e-05 ***
Other_Female_30_to_39_years    -3.40258912  0.35368437  -9.6204 < 2.2e-16 ***
Other_Female_40_to_49_years     1.34563633  0.42503994   3.1659 0.0015825 ** 
Other_Female_50_to_64_years     2.93990932  0.33830653   8.6901 < 2.2e-16 ***
Other_Female_65_years_and_over  2.36026239  0.20422580  11.5571 < 2.2e-16 ***
Other_Male_10_to_19_years       0.07481449  0.47835310   0.1564 0.8757423    
Other_Male_20_to_29_years       1.62895925  0.25740603   6.3284 3.420e-10 ***
Other_Male_30_to_39_years       3.17421278  0.41184489   7.7073 2.566e-14 ***
Other_Male_40_to_49_years      -1.58494177  0.44840281  -3.5346 0.0004229 ***
Other_Male_50_to_64_years      -3.91523867  0.37399898 -10.4686 < 2.2e-16 ***
Other_Male_65_years_and_over   -4.16596244  0.36860536 -11.3020 < 2.2e-16 ***
Unemployment_rate              -0.00545734  0.00436374  -1.2506 0.2113054    
Poverty_rate                   -0.00572362  0.00253162  -2.2609 0.0239357 *  
Population_log                 -0.21716335  0.08452664  -2.5692 0.0103068 *  
police_per_100k_lag             0.00069547  0.00013331   5.2171 2.118e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    43.211
Residual Sum of Squares: 23.647
R-Squared:      0.45275
Adj. R-Squared: 0.40355
F-statistic: 25.8088 on 41 and 1279 DF, p-value: < 2.22e-16

LOTT_OUTPUT_TIDY <- tidy(LOTT_OUTPUT, conf.int = 0.95)

LOTT_OUTPUT_TIDY$Analysis <- "Analysis 2"

RTC coefficient comparison

Now let’s make a plot to compare the coeffecient estimate for the Right-to-carry law adoption variable in each model.

First we will combine model fit information for this coefficient for each model.

comparing_analyses <- DONOHUE_OUTPUT_TIDY %>%
  bind_rows(LOTT_OUTPUT_TIDY) %>%
  filter(term == "RTC_LAWTRUE")

comparing_analyses

# A tibble: 2 x 8
  term        estimate std.error statistic p.value conf.low conf.high Analysis  
  <chr>          <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl> <chr>     
1 RTC_LAWTRUE   0.0180    0.0166      1.08 0.280    -0.0146    0.0506 Analysis 1
2 RTC_LAWTRUE  -0.0469    0.0164     -2.85 0.00438  -0.0791   -0.0147 Analysis 2

We can see that for the first analysis (similar to the Donohue et al. {target="_blank“} study) the coefficient estimate for the presence of a permissive Right-to-carry law is positive, while for the second analysis (similar to the Lott and Mustard study) the coefficient estimate is negative. Thus in the first analysi we could conclude that the effect of adopting permissive right-to-carry laws may be associated with increases in violent crime (although this was not a significant result (in contrast with the real Donohue et al. {target=”_blank"} study )); while in the other analysis we could conclude that the laws may be associated with decreases in violent crime.

Let’s make a plot of this finding. We will show errorbars for the coefficient estimates for both analyses using the geom_errorbar() function of the ggplot2 package. This requires specifying the minimum and maximum for our errorbar, which in our case we would like to be the low and high values of our confidence intervals for the coefficient estimates. We will also add a horizontal line at y = 0 using the geom_hline() function of the ggplot2 package.

Finally we will add arrows to emphasize the difference in the direction of the findings using the geom_segment() function of the ggplot2 package. Using the arrow() function, we can specify detials about the arrow we would like to add.

#library(grid)

comparing_analyses_plot <- ggplot(comparing_analyses) + 
  geom_point(aes(x = Analysis, y = estimate)) +
  geom_errorbar(aes(x = Analysis, ymin = conf.low, ymax = conf.high), width = 0.25) + 
  geom_hline(yintercept = 0, color = "red") +
  scale_y_continuous(breaks = seq(-0.2, 0.2, by = 0.05),
                     labels = seq(-0.2, 0.2, by = 0.05),
                     limits = c(-0.2,0.2)) +
  geom_segment(aes(x = 1, y = 0.125, xend = 1, yend = 0.175),
               arrow = arrow(angle = 45, ends = "last", type = "open"),
               size = 2,
               color = "green",
               lineend = "butt",
               linejoin = "mitre") +
  geom_segment(aes(x = 2, y = -0.125, xend = 2, yend = -0.175),
               arrow = arrow(angle = 45, ends = "last", type = "open"),
               size = 2,
               color = "red",
               lineend = "butt",
               linejoin = "mitre") +
  theme_minimal() + 
  theme(axis.title.x = element_blank(),
        axis.text = element_text(size = 12)) +
  labs(title = "Effect estimate on ln(violent crimes per 100,000 people)",
       y = "Effect estimate (95% CI)")

comparing_analyses_plot

We can see that most of the possible range of values for the estimate in analysis 1 are positive, while they are all negative for analysis 2.

Multicollinearity analysis

How did the above happen?

The analysis dataframes are very similar yet rendered very different results.

Recall that the only difference is the number of demographic variables. The number of rows or observations is the same. We can use the all_equal() function of the dplyr package to compare the number of columns of our Donohue-like data and our Lott-like data.

all_equal(target = DONOHUE_DF,
          current = LOTT_DF,
          ignore_col_order = TRUE,
          ignore_row_order = TRUE)

- different number of columns: 20 vs 50

Using the base dim() function we can also look at the number of rows for each and see that the number of observatiosn is the same for both datasets.

dim(DONOHUE_DF)[1]

[1] 1395

dim(LOTT_DF)[1]

[1] 1395

The only difference between the two dataframes rests in how the demographic variables were parameterized.

DONOHUE_DF %>%
  dplyr::select(contains("years")) %>%
  colnames()

[1] "Black_Male_15_to_19_years" "Black_Male_20_to_39_years"
[3] "Other_Male_15_to_19_years" "Other_Male_20_to_39_years"
[5] "White_Male_15_to_19_years" "White_Male_20_to_39_years"

LOTT_DF %>%
  dplyr::select(contains("years")) %>%
  colnames()

 [1] "Black_Female_10_to_19_years"    "Black_Female_20_to_29_years"   
 [3] "Black_Female_30_to_39_years"    "Black_Female_40_to_49_years"   
 [5] "Black_Female_50_to_64_years"    "Black_Female_65_years_and_over"
 [7] "Black_Male_10_to_19_years"      "Black_Male_20_to_29_years"     
 [9] "Black_Male_30_to_39_years"      "Black_Male_40_to_49_years"     
[11] "Black_Male_50_to_64_years"      "Black_Male_65_years_and_over"  
[13] "Other_Female_10_to_19_years"    "Other_Female_20_to_29_years"   
[15] "Other_Female_30_to_39_years"    "Other_Female_40_to_49_years"   
[17] "Other_Female_50_to_64_years"    "Other_Female_65_years_and_over"
[19] "Other_Male_10_to_19_years"      "Other_Male_20_to_29_years"     
[21] "Other_Male_30_to_39_years"      "Other_Male_40_to_49_years"     
[23] "Other_Male_50_to_64_years"      "Other_Male_65_years_and_over"  
[25] "White_Female_10_to_19_years"    "White_Female_20_to_29_years"   
[27] "White_Female_30_to_39_years"    "White_Female_40_to_49_years"   
[29] "White_Female_50_to_64_years"    "White_Female_65_years_and_over"
[31] "White_Male_10_to_19_years"      "White_Male_20_to_29_years"     
[33] "White_Male_30_to_39_years"      "White_Male_40_to_49_years"     
[35] "White_Male_50_to_64_years"      "White_Male_65_years_and_over"

Clearly, this had an effect on the results of the analysis.

Let’s explore how this occured.

When seemingly independent variables are highly related to one another, the relationships estimated in an analysis may be distorted.

In regression analysis, this distortion is often a by-product of a violation of the independence assumption. This distortion, if large enough, can impact statistical inference.

The phenomona called multicollinearity occurs when independent variables are highly related to one another

There are several ways we can diagnose multicollinearity.

Correlation

One way we can evaluate the relationships between variables is by examining the correlation between variable pairs.

It is important to note that multicollinearity and correlation are not one and the same. Correlation can be thought of as the strength of the relationship between variables. On the other hand, multicollinearity can be thought of as the the violation of the independence assumption that is a consequence of this correlation in a regression analysis.

Scatterplots

One way to look at correlatin across pairs of variables is to use the ggpairs() function of the GGally package.

colnames(DONOHUE_DF)

 [1] "YEAR"                      "STATE"                    
 [3] "Black_Male_15_to_19_years" "Black_Male_20_to_39_years"
 [5] "Other_Male_15_to_19_years" "Other_Male_20_to_39_years"
 [7] "White_Male_15_to_19_years" "White_Male_20_to_39_years"
 [9] "Unemployment_rate"         "Poverty_rate"             
[11] "Viol_crime_count"          "Population"               
[13] "police_per_100k_lag"       "RTC_LAW_YEAR"             
[15] "RTC_LAW"                   "TIME_0"                   
[17] "TIME_INF"                  "Viol_crime_rate_1k"       
[19] "Viol_crime_rate_1k_log"    "Population_log"

DONOHUE_DF %>% 
  dplyr::select(RTC_LAW,
                Viol_crime_rate_1k_log,
                Unemployment_rate,
                Poverty_rate,
                Population_log) %>% 
  ggpairs(.,
          columns = c(2:5),
          lower = list(continuous = wrap("smooth_loess",
                                         color = "red",
                                         alpha = 0.5,
                                         size = 0.1)))

We can see that for the non-demographic variables, there is very little correlation between the pairs of variables. Only the unemployment rate and the poverty rate show relatively strong correlation, as one might expect.

Heatmaps

Another way to look at correlation if we have many variables is to use heatmaps.

Let’s to this now for the deomographic variables for each analysis.

The ggcorrplot() function of the ggpcorplot package is one way to create such a heatmap.

This requires first calculating the correaltion values using the cor() function of the stats package.

cor_DONOHUE_dem <- cor(DONOHUE_DF %>% dplyr::select(contains("_years")))

corr_mat_DONOHUE <- ggcorrplot(cor_DONOHUE_dem,
           tl.cex = 6,
           hc.order = TRUE,
           colors = c("red",
                      "white",
                      "red"),
           outline.color = "transparent",
           title = "Correlation Matrix, Analysis 1",
           legend.title = TeX("$\\rho$"))

corr_mat_DONOHUE

cor_LOTT_dem <- cor(LOTT_DF %>% dplyr::select(contains("_years")))

corr_mat_LOTT <- ggcorrplot(cor_LOTT_dem,
           tl.cex = 6,
           hc.order = TRUE,
           colors = c("red",
                      "white",
                      "red"),
           outline.color = "transparent",
           title = "Correlation Matrix, Analysis 2",
           legend.title = TeX("$\\rho$"))

corr_mat_LOTT

We can see that many of the demographic variables are highly correlated with one another.

The presence of correlation bewteen variables suggests that we might have multicollinearity. However it does not necessarily mean that we do. So how can we assess this?

Coefficient estimate instability

One way to look at the possible influence of multicollinearit is to look at the stability of the coefficient estimates.

We will focus on the RTC_LAW variable coefficient estimate, as this is of particular interest in our case.

To do so we will perform multiple iterations of our analysis, but we will remove one observation and see if that changes our coefficient estimate results.

To do this we will use some functions in the rsample package which is very useful for splitting data in various ways.

We will use the loo_cv() function which stands for leave one out cross validation. This will allow us to split our data into every possible subset where a unique observation is left out of the data.

This function will however only prepare the data to be split.

To actually get the remaining data after the removal of the observation that is left out when need to use a function called training(). This is because these functions are often used for in machine learning applications where the data is split between a larger training set and a smaller testing set. Thus we want the larger \(n-1\) subset, as opposed to the single value that is removed, (which we could get with the testing() function)

avocado move this:Note that this causes our data to be an unbalanced panel. This does not require any adjustment to the code to model the data, but you will notice that the output will now say “unbalanced”.

Click here to see an example of how this works.

First we will make a toy dataset that is very simle called test using the tibble() function of the tidyr package:

test <-tidyr::tibble(x = c(1,2,3))
test

# A tibble: 3 x 1
      x
  <dbl>
1     1
2     2
3     3

Now we will use the loo_cv() to create leave one out splits:

test_samples <- test %>% loo_cv()
test_samples

# Leave-one-out cross-validation 
# A tibble: 3 x 2
  splits        id       
  <list>        <chr>    
1 <split [2/1]> Resample1
2 <split [2/1]> Resample2
3 <split [2/1]> Resample3

We can take a look at one individual split using the pull() function:

pull(test_samples, splits)

[[1]]
<Analysis/Assess/Total>
<2/1/3>

[[2]]
<Analysis/Assess/Total>
<2/1/3>

[[3]]
<Analysis/Assess/Total>
<2/1/3>

Here you can see that 2 values are intended for the training set (aslo called Analysis set), 1 value is intended for the testing set (also called Assessment set), and 3 values were present initially.

Now we will use the training() function to get the data without the obersvation that is set aside. Here is the data for the first subset:

training(pull(test_samples, splits)[[1]])

# A tibble: 2 x 1
      x
  <dbl>
1     1
2     2

Now we will use the map() function of purrr to get all possible training subset of the data.

test_subsets <- map(pull(test_samples, splits), training)
test_subsets

[[1]]
# A tibble: 2 x 1
      x
  <dbl>
1     1
2     2

[[2]]
# A tibble: 2 x 1
      x
  <dbl>
1     2
2     3

[[3]]
# A tibble: 2 x 1
      x
  <dbl>
1     1
2     3

We can see that there are 3 possible subsets that leave one value out. All 3 possible subsets are created using this method. This method will always create the same number of subsets as there are unqiue values or rows in the data.

Now we will use this method with the data from our Donohue-like analysis, since this data has dim(d_panel_DONOHUE)[1] rows, dim(d_panel_DONOHUE)[1] subsets will be created that leave out one row.

First we will create the splits using the loo_cv() function:

set.seed(124)
DONOHUE_splits <- d_panel_DONOHUE %>% loo_cv()
DONOHUE_splits

# Leave-one-out cross-validation 
# A tibble: 1,395 x 2
   splits           id        
   <list>           <chr>     
 1 <split [1.4K/1]> Resample1 
 2 <split [1.4K/1]> Resample2 
 3 <split [1.4K/1]> Resample3 
 4 <split [1.4K/1]> Resample4 
 5 <split [1.4K/1]> Resample5 
 6 <split [1.4K/1]> Resample6 
 7 <split [1.4K/1]> Resample7 
 8 <split [1.4K/1]> Resample8 
 9 <split [1.4K/1]> Resample9 
10 <split [1.4K/1]> Resample10
# … with 1,385 more rows

Now we will use the training() function to select the remaining data without the value that was removed for each split:

# To get all the data subsets
DONOHUE_subsets <-map(pull(DONOHUE_splits, splits), training)

glimpse(DONOHUE_subsets[[1]])

Rows: 1,394
Columns: 20
$ YEAR                      <fct> 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1…
$ STATE                     <fct> Alaska, Alaska, Alaska, Alaska, Alaska, Ala…
$ Black_Male_15_to_19_years <pseries> 0.1670456, 0.1732299, 0.1737069, 0.1709…
$ Black_Male_20_to_39_years <pseries> 0.9933775, 1.0028219, 1.0204445, 1.0312…
$ Other_Male_15_to_19_years <pseries> 1.1297816, 1.1244412, 1.0698208, 0.9882…
$ Other_Male_20_to_39_years <pseries> 2.963329, 2.974775, 3.015071, 3.008048,…
$ White_Male_15_to_19_years <pseries> 3.627805, 3.558261, 3.391844, 3.222002,…
$ White_Male_20_to_39_years <pseries> 18.28852, 18.12821, 18.10666, 17.90600,…
$ Unemployment_rate         <pseries> 9.6, 9.4, 9.9, 9.9, 9.8, 9.7, 10.9, 10.…
$ Poverty_rate              <pseries> 9.6, 9.0, 10.6, 12.6, 9.6, 8.7, 11.4, 1…
$ Viol_crime_count          <pseries> 1919, 2537, 2732, 2940, 3108, 3031, 304…
$ Population                <pseries> 404680, 418519, 449608, 488423, 513697,…
$ police_per_100k_lag       <pseries> 194.7218, 200.2299, 191.0553, 364.2335,…
$ RTC_LAW_YEAR              <pseries> 1995, 1995, 1995, 1995, 1995, 1995, 199…
$ RTC_LAW                   <pseries> FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
$ TIME_0                    <pseries> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ TIME_INF                  <pseries> 2010, 2010, 2010, 2010, 2010, 2010, 201…
$ Viol_crime_rate_1k        <pseries> 4.742018, 6.061851, 6.076404, 6.019373,…
$ Viol_crime_rate_1k_log    <pseries> 1.556463, 1.802015, 1.804413, 1.794983,…
$ Population_log            <pseries> 12.91085, 12.94448, 13.01613, 13.09894,…

length(DONOHUE_subsets)

[1] 1395

As expected the first subset has 1,394 rows and there are 1395 subsets.

Let’s see what obsevation was left out in the first subset:

d_panel_DONOHUE %>%
  filter(! rownames(d_panel_DONOHUE)  %in% rownames(DONOHUE_subsets[[1]]))

           YEAR STATE Black_Male_15_to_19_years Black_Male_20_to_39_years
Texas-1988 1988 Texas                 0.5599219                  2.091018
           Other_Male_15_to_19_years Other_Male_20_to_39_years
Texas-1988                0.09824717                 0.4413413
           White_Male_15_to_19_years White_Male_20_to_39_years
Texas-1988                  3.447339                  14.79001
           Unemployment_rate Poverty_rate Viol_crime_count Population
Texas-1988               7.3           18           109499   16667146
           police_per_100k_lag RTC_LAW_YEAR RTC_LAW TIME_0 TIME_INF
Texas-1988            298.2274         1996   FALSE   1980     2010
           Viol_crime_rate_1k Viol_crime_rate_1k_log Population_log
Texas-1988           6.569751               1.882476       16.62895

# Another way to check is to use:
DONOHUE_removed <-map(pull(DONOHUE_splits, splits), testing)

DONOHUE_removed[[1]]

           YEAR STATE Black_Male_15_to_19_years Black_Male_20_to_39_years
Texas-1988 1988 Texas                 0.5599219                  2.091018
           Other_Male_15_to_19_years Other_Male_20_to_39_years
Texas-1988                0.09824717                 0.4413413
           White_Male_15_to_19_years White_Male_20_to_39_years
Texas-1988                  3.447339                  14.79001
           Unemployment_rate Poverty_rate Viol_crime_count Population
Texas-1988               7.3           18           109499   16667146
           police_per_100k_lag RTC_LAW_YEAR RTC_LAW TIME_0 TIME_INF
Texas-1988            298.2274         1996   FALSE   1980     2010
           Viol_crime_rate_1k Viol_crime_rate_1k_log Population_log
Texas-1988           6.569751               1.882476       16.62895

It looks like the Texas data from 1988 was removed from the first split.

OK, so now let’s fit our panel regression on the first subset of data like we did previously:

 subset_1_result<-plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        data = DONOHUE_subsets[[1]],
                       index = c("STATE","YEAR"),
                       model = "within",
                      effect = "twoways")
summary(subset_1_result)

Twoways effects Within Model

Call:
plm(formula = Viol_crime_rate_1k_log ~ RTC_LAW + White_Male_15_to_19_years + 
    White_Male_20_to_39_years + Black_Male_15_to_19_years + Black_Male_20_to_39_years + 
    Other_Male_15_to_19_years + Other_Male_20_to_39_years + Unemployment_rate + 
    Poverty_rate + Population_log + police_per_100k_lag, data = DONOHUE_subsets[[1]], 
    effect = "twoways", model = "within", index = c("STATE", 
        "YEAR"))

Unbalanced Panel: n = 45, T = 30-31, N = 1394

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-0.5795659 -0.0893732 -0.0014097  0.0865870  1.1118927 

Coefficients:
                             Estimate  Std. Error t-value  Pr(>|t|)    
RTC_LAWTRUE                0.01812436  0.01664364  1.0890 0.2763694    
White_Male_15_to_19_years -0.00103284  0.02724763 -0.0379 0.9697687    
White_Male_20_to_39_years  0.03462260  0.00973037  3.5582 0.0003868 ***
Black_Male_15_to_19_years -0.05699742  0.05747236 -0.9917 0.3215097    
Black_Male_20_to_39_years  0.12591876  0.01931901  6.5179 1.016e-10 ***
Other_Male_15_to_19_years  0.69114956  0.11325143  6.1028 1.371e-09 ***
Other_Male_20_to_39_years -0.30242747  0.03812860 -7.9318 4.603e-15 ***
Unemployment_rate         -0.01698131  0.00490345 -3.4631 0.0005512 ***
Poverty_rate              -0.00782727  0.00295795 -2.6462 0.0082384 ** 
Population_log            -0.17899715  0.06044257 -2.9614 0.0031173 ** 
police_per_100k_lag        0.00060326  0.00013692  4.4058 1.140e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    43.198
Residual Sum of Squares: 36.701
R-Squared:      0.15039
Adj. R-Squared: 0.095183
F-statistic: 21.0489 on 11 and 1308 DF, p-value: < 2.22e-16

Indeed, we can see that now we have an unbalanced panel with N = 1394 observations instead of 1395, as expected.

Now that we have our subsets, we want to write a function to fit a panel regression using plm()on each subsets. See this case study for more information on writing functions.

avocado add bootstarp description maybe link about cross validation- testing and training sets

fit_nls_on_bootstrap_DONOHUE <- function(subset){
                    plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        data = data.frame(subset),
                       index = c("STATE","YEAR"),
                       model = "within",
                      effect = "twoways")
}

Now we can apply this function to each of our subsets simultaneously using the map() function of the purrr package.

subsets_models_DONOHUE <- map(DONOHUE_subsets, fit_nls_on_bootstrap_DONOHUE)

subsets_models_DONOHUE <- subsets_models_DONOHUE %>%
  map(tidy)

Great! Now we want to do the same thing for the Lott data.

set.seed(124)
LOTT_splits <- d_panel_LOTT %>% loo_cv()

# To get all the data subsets:
LOTT_subsets <-map(pull(LOTT_splits, splits), training)

We need to create a different function to fit the data to account for the larger number of demographic variables. We will use the formula that we made previously.

fit_nls_on_bootstrap_LOTT <- function(split){
  plm(LOTT_fmla,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}


subsets_models_LOTT <- map(LOTT_subsets, fit_nls_on_bootstrap_LOTT)

subsets_models_LOTT <- subsets_models_LOTT %>%
  map(tidy)

Now we will combine the output so that we can make a plot to visualize the results that we obtained. First let’s name each subset that we created.

names(subsets_models_DONOHUE) <-     paste0("DONOHUE_",1:length(subsets_models_DONOHUE))

names(subsets_models_LOTT) <- 
paste0("LOTT_",1:length(subsets_models_LOTT))

Now we can combine the tibbles within the list of tibbles for the subsets_models_DONOHUE and subsets_models_LOTT data.

To do this we will use the bind_rows() function of the dplyr package with the .id = "ID" argument, which will create a new variable called ID that will list the name of the tibble the data came from.

Then we will combine the data from both the Donohue and Lott simulations.

simulations_DONOHUE <- subsets_models_DONOHUE %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 1")

simulations_LOTT <- subsets_models_LOTT %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 2")

simulations <- bind_rows(simulations_DONOHUE,
                         simulations_LOTT)

head(simulations)

# A tibble: 6 x 7
  ID        term                 estimate std.error statistic  p.value Analysis 
  <chr>     <chr>                   <dbl>     <dbl>     <dbl>    <dbl> <chr>    
1 DONOHUE_1 RTC_LAWTRUE           0.0181    0.0166     1.09   2.76e- 1 Analysis…
2 DONOHUE_1 White_Male_15_to_19… -0.00103   0.0272    -0.0379 9.70e- 1 Analysis…
3 DONOHUE_1 White_Male_20_to_39…  0.0346    0.00973    3.56   3.87e- 4 Analysis…
4 DONOHUE_1 Black_Male_15_to_19… -0.0570    0.0575    -0.992  3.22e- 1 Analysis…
5 DONOHUE_1 Black_Male_20_to_39…  0.126     0.0193     6.52   1.02e-10 Analysis…
6 DONOHUE_1 Other_Male_15_to_19…  0.691     0.113      6.10   1.37e- 9 Analysis…

tail(simulations)

# A tibble: 6 x 7
  ID       term                  estimate std.error statistic  p.value Analysis 
  <chr>    <chr>                    <dbl>     <dbl>     <dbl>    <dbl> <chr>    
1 LOTT_13… Other_Male_50_to_64_… -3.91e+0  0.375       -10.4  1.66e-24 Analysis…
2 LOTT_13… Other_Male_65_years_… -4.16e+0  0.369       -11.3  3.97e-28 Analysis…
3 LOTT_13… Unemployment_rate     -5.43e-3  0.00437      -1.24 2.14e- 1 Analysis…
4 LOTT_13… Poverty_rate          -5.75e-3  0.00253      -2.27 2.33e- 2 Analysis…
5 LOTT_13… Population_log        -2.16e-1  0.0846       -2.55 1.08e- 2 Analysis…
6 LOTT_13… police_per_100k_lag    6.96e-4  0.000133      5.22 2.07e- 7 Analysis…

Now we will make a jitter plot using the geom_jitter() function of the coefficient estimates of the RTC_LAWTRUE variable for each simulation.

simulation_plot <- simulations %>%
  filter(term=="RTC_LAWTRUE") %>%
  ggplot(aes(x = Analysis, y = estimate)) + 
  geom_jitter(alpha = 0.25,
              width = 0.1) + 
  labs(title = "Coefficient instability",
       subtitle = "Estimates sensitive to observation deletions",
       x = "Term",
       y = "Coefficient",
       caption = "Results from simulations") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

simulation_plot

from Michael

sims <- 250

# DONOHUE
samps_DONOHUE <- lapply(rep(dim(DONOHUE_DF)[1]-1, sims),
       function(x)DONOHUE_DF[sample(nrow(DONOHUE_DF),
                                     size = x, replace = FALSE),])

fit_nls_on_bootstrap_DONOHUE <- function(split){
  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_DONOHUE <- lapply(samps_DONOHUE, fit_nls_on_bootstrap_DONOHUE)

samps_models_DONOHUE <- samps_models_DONOHUE %>%
  map(tidy)

names(samps_models_DONOHUE) <- paste0("DONOHUE_",1:length(samps_models_DONOHUE))

simulations_DONOHUE <- samps_models_DONOHUE %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 1")

## LOTT

samps_LOTT <- lapply(rep(round(dim(LOTT_DF)[1]/2), sims),
       function(x) LOTT_DF[sample(nrow(LOTT_DF),
                                  size = x, replace = FALSE),])

fit_nls_on_bootstrap_LOTT <- function(split){
  plm(LOTT_fmla,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_LOTT <- lapply(samps_LOTT, fit_nls_on_bootstrap_LOTT)

samps_models_LOTT <- samps_models_LOTT %>%
  map(tidy)

names(samps_models_LOTT) <- paste0("LOTT_",1:length(samps_models_LOTT))

simulations_LOTT <- samps_models_LOTT %>%
  bind_rows(.id = "Analysis") %>%
  mutate(Analysis = "Analysis 2")

simulations <- bind_rows(simulations_DONOHUE,
                         simulations_LOTT)

simulation_plot <- simulations %>%
  filter(term=="RTC_LAWTRUE") %>%
  ggplot(aes(x = Analysis, y = estimate)) + 
  geom_jitter(alpha = 0.25,
              width = 0.1) + 
  labs(title = "Coefficient instability",
       subtitle = "Estimates sensitive to observation deletions",
       x = "Term",
       y = "Coefficient",
       caption = "Results from simulations") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

simulation_plot

VIF

design.matrix <- as.data.frame(model.matrix(DONOHUE_OUTPUT))

design.matrix$Viol_crime_rate_1k_log <- plm::Within(
  d_panel_DONOHUE$Viol_crime_rate_1k_log)

lm_DONOHUE <- lm(Viol_crime_rate_1k_log ~
                        RTC_LAWTRUE + # logical class changes variable name after inital model
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log +
               police_per_100k_lag,
             data = design.matrix)


vif(lm_DONOHUE)

              RTC_LAWTRUE White_Male_15_to_19_years White_Male_20_to_39_years 
                 1.097853                  1.172339                  1.738459 
Black_Male_15_to_19_years Black_Male_20_to_39_years Other_Male_15_to_19_years 
                 1.344193                  1.653712                  1.586648 
Other_Male_20_to_39_years         Unemployment_rate              Poverty_rate 
                 1.529688                  1.244667                  1.270321 
           Population_log       police_per_100k_lag 
                 1.153933                  1.204491

vif_DONOHUE <- vif(lm_DONOHUE)

vif_DONOHUE <- vif_DONOHUE %>%
  as_tibble() %>%
  cbind(., names(vif_DONOHUE)) %>%
  as_tibble()
  
colnames(vif_DONOHUE) <- c("VIF", "Variable")

max_vif_DONOHUE <- max(vif(lm_DONOHUE))

design.matrix <- as.data.frame(model.matrix(LOTT_OUTPUT))

design.matrix$Viol_crime_rate_1k_log <- plm::Within(
  d_panel_LOTT$Viol_crime_rate_1k_log)

LOTT_variables_ols <- LOTT_DF %>%
  dplyr::select(RTC_LAW,
                contains(c("White","Black","Other")),
                Unemployment_rate,
                Poverty_rate,
                Population_log,
                police_per_100k_lag) %>%
  colnames() %>%
  str_replace("RTC_LAW", "RTC_LAWTRUE") # logical class changes variable name after inital model

LOTT_fmla_ols <- as.formula(paste("Viol_crime_rate_1k_log ~",
                              paste(LOTT_variables_ols, collapse = " + ")
                              )
                        )

lm_LOTT <- lm(LOTT_fmla_ols,
             data = design.matrix)

vif(lm_LOTT)

                   RTC_LAWTRUE    White_Female_10_to_19_years 
                      1.621662                     127.920555 
   White_Female_20_to_29_years    White_Female_30_to_39_years 
                     42.269637                      49.635014 
   White_Female_40_to_49_years    White_Female_50_to_64_years 
                     37.550101                      36.451868 
White_Female_65_years_and_over      White_Male_10_to_19_years 
                     12.866751                     126.824984 
     White_Male_20_to_29_years      White_Male_30_to_39_years 
                     39.248785                      73.008959 
     White_Male_40_to_49_years      White_Male_50_to_64_years 
                     31.613855                      52.774694 
  White_Male_65_years_and_over    Black_Female_10_to_19_years 
                     13.285326                     335.136906 
   Black_Female_20_to_29_years    Black_Female_30_to_39_years 
                    106.644486                      79.058455 
   Black_Female_40_to_49_years    Black_Female_50_to_64_years 
                     98.434064                      66.888057 
Black_Female_65_years_and_over      Black_Male_10_to_19_years 
                     49.715869                     320.740453 
     Black_Male_20_to_29_years      Black_Male_30_to_39_years 
                     89.297151                      89.267356 
     Black_Male_40_to_49_years      Black_Male_50_to_64_years 
                     92.498486                      64.538516 
  Black_Male_65_years_and_over    Other_Female_10_to_19_years 
                     37.960126                     142.283700 
   Other_Female_20_to_29_years    Other_Female_30_to_39_years 
                     64.966861                      54.511835 
   Other_Female_40_to_49_years    Other_Female_50_to_64_years 
                    224.567085                     131.463113 
Other_Female_65_years_and_over      Other_Male_10_to_19_years 
                     82.394398                     151.930450 
     Other_Male_20_to_29_years      Other_Male_30_to_39_years 
                     54.620045                      62.267344 
     Other_Male_40_to_49_years      Other_Male_50_to_64_years 
                    244.698473                     174.184553 
  Other_Male_65_years_and_over              Unemployment_rate 
                     53.532299                       1.497864 
                  Poverty_rate                 Population_log 
                      1.412397                       3.426475 
           police_per_100k_lag 
                      1.732745

vif_LOTT <- vif(lm_LOTT)

vif_LOTT <- vif_LOTT %>%
  as_tibble() %>%
  cbind(., names(vif_LOTT)) %>%
  as_tibble()
  
colnames(vif_LOTT) <- c("VIF", "Variable")

max_vif_LOTT <- max(vif(lm_LOTT))

[1] 1.738459

[1] 335.1369

\[\frac{1}{1-R_{i}^{2}}\]

vif_DONOHUE$Analysis <- "Analysis 1"
vif_LOTT$Analysis <- "Analysis 2"

vif_df <- rbind(vif_DONOHUE,
                vif_LOTT)

vif_plot <- vif_df %>%
  ggplot(aes(x = Analysis, y = VIF)) +
  geom_jitter(width = 0.1, alpha = 0.5, size = 2) +
  geom_hline(yintercept = 10, color = "red") +
  scale_y_continuous(trans = 'log10',
                     limits = c(1,1000)) +
  labs(title = "Variance inflation factors") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

vif_plot

Synthesis

Data Visualization

Summary

Suggested Homework

Additional Information

*Helpful Links

Tidyverse
Writing functions
Longitudinal studies
Panel data
Confidence intervals
Linear regression model

For more information on linear regression see this book and this case study.

avocado fill this out

https://rpubs.com/rslbliss/fixed_effects

http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

https://stats.stackexchange.com/questions/99236/effects-in-panel-models-individual-time-or-twoways

Acknowledgements

We would like to acknowledge Daniel Webster for assisting in framing the major direction of the case study.

We would also like to acknowledge the Bloomberg American Health Initiative for funding this work.

---
title: "Open Case Studies: Examination of Multicollinearity Influence on Inference Using Right-to-Carry Gun Law and Violent Crime Data"
author: "Michael Ontiveros, Carrie Wright, PhD."
css: style.css
output:
  html_document:
    self_contained: yes
    code_download: yes
    highlight: tango
    number_sections: no
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
---

<style>
#TOC {
  background: url("https://opencasestudies.github.io/img/logo.jpg");
  background-size: contain;
  padding-top: 240px !important;
  background-repeat: no-repeat;
}
</style>


---


```{r setup, include=FALSE}
knitr::opts_chunk$set(include = TRUE, comment = NA, echo = TRUE,
                      message = FALSE, warning = FALSE, cache = FALSE, fig.width=10, fig.height=7,
                      fig.align = "center", out.width = '90%')
library(here)
library(knitr)

```




#### {.outline }
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "mainplot.png"))
```

####

## {.disclaimer_block}

**Disclaimer**: The purpose of the [Open Case Studies](https://opencasestudies.github.io){target="_blank"} project is **to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data**. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. 

## {.license_block}

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 [(CC BY-NC 3.0)](https://creativecommons.org/licenses/by-nc/3.0/us/){target="_blank"}  United States License.

# **Motivation**
*** 

This case study will introduce the topic of multicolinearity. We will do so by showcasing a real world example where multicolinearity in part resulted in historically contriversial and conflicting findings about the influence of the adoption of right-to-carry (RTC) concealed handgun laws on violent crime rates in the United States. 

We will focus on two articles:

1) The first analysis by [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} published in 1996 suggests that RTC laws reduce violent crime. Lott authored a book extending these findings in 1998 called [***More Guns, Less Crime***](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"}.

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Lott.png"))
```

2) The second analysis is a recent article by [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} published in 2017 that suggests that RTC laws increase violent crime. Donohue has also published previous articles with titles such as [***"Shooting down the "More Guns, Less Crime" Hypothesis***](https://www.jstor.org/stable/1229603?seq=1){target="_blank"} 

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Donohue.png"))
```

This has been a controversial topic as many other articles also had conflicting results. See [here](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"} for a list of studies.

The [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article discusses how there are many other important methodolical aspects besides multicolinearity that could account for the historically conflicting results in these previous papers.

In fact, nearly every aspect of the data analysis process was different between the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analysis and the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} analysis.

```{r, echo=FALSE, out.height = '75%', out.width = '75%', fig.align='center'}
knitr::include_graphics(here("img", "Educational_Graphic1.jpg"))
```

However, we will focus particularly on multicolinearity and we will explore how it can influence linear regression analyses and result in different conclusions. 

This analysis will demonstrate how methodological details can be critically influential for our overall conclusions and can result in important policy related consequences. This [article]((https://www.nber.org/papers/w23510.pdf){target="_blank"}) will provide a basis for the motivation. 

#### {.reference_block}

John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. *Journal of Empirical Legal Studies*, 16,2 (2019).

David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. *Coase-Sandor Institute for Law & Economics* Working Paper No. 41, (1996).

####


Here you can see the differences in the data used in the featured RTC articles:


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2.png'))
```


We will perform analyses similar to those in these articles, however **we will not try to recreate them**, instead we will simplify our analysis to allow us to focus on multicolinearity.


Therefore we will use a subset of the listed explanatory variables and they will be consistent for both analyses that we will perform, with the exception that one analysis will have 6 demographic variables like the analysis in the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article and the other will have 36 demogrpahic variables like the analysis in the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} article.


# **Main Question**
*** 

#### {.main_question_block}
<b><u> Our main question: </u></b>

1) How does the inclusion of different numbers of age groups influence the results of an analysis of right to carry laws and violence rates?

####



# **Learning Objectives** 
*** 

<u>**Statistical Learning Objectives:**</u> 

1) what multicollinearity is and how it can influence linear regression coefficients  
2) how to look for the presence of multicollinarity and determine the severity
3) the difference between multicollinearity and correlation 


<u>**Data science Learning Objectives:**</u>

1) data import of many different file types with special cases (`readr`, `readxl`, `pdftools`)
2) joining data from multiple sources (`dplyr`)  
3) working with character strings (`stringr`)
4) data comparisons (`dplyr` and `janitor`)
5) reshaping data into different formats (`tidyr`)  
6) visualizations (`ggplot2`) 
7) perform iterative simulations (`rsample`)



We will especially focus on using packages and functions from the [`Tidyverse`](https://www.tidyverse.org/){target="_blank"}, such as `dplyr` and `ggplot2`. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.


```{r, out.width = "20%", echo = FALSE, fig.align ="center"}
include_graphics("https://tidyverse.tidyverse.org/logo.png")
```

# **Context**
***

So what exactly is a **right-to-carry law**?

It is a law thatspecifies if and how citizens are allowed to have a firearm on their person or nearby (for example in the citizen's car) in public. 

The [Second Amendment](https://en.wikipedia.org/wiki/Second_Amendment_to_the_United_States_Constitution){target="_blank"} to the United States Constitution guarantees the right to "keep and bear arms". The amendment was ratified in 1791 as part of the [Bill of Rights](https://en.wikipedia.org/wiki/United_States_Bill_of_Rights){target="_blank"}.

```{r, echo=FALSE, out.height = '50%', out.width = '50%', fig.align='center'}
knitr::include_graphics("https://upload.wikimedia.org/wikipedia/commons/7/79/Bill_of_Rights_Pg1of1_AC.jpg")
```

However, there are no federal laws about carrying firearms in public. 

These laws are created and enforced at the state level. Sates vary greatly in their laws about the right to carry firearms. Some require extensive effort to obtain a permit to legally carry a firearm, while other states require very minimal effort to legally carry a firearm.


According to Wikipedia about the history of right-to-carry policies in the United States:

> Public perception on concealed carry vs open carry has largely flipped. In the early days of the United States, open carrying of firearms, long guns and revolvers was a common and well-accepted practice. Seeing guns carried openly was not considered to be any cause for alarm. Therefore, anyone who would carry a firearm but attempt to conceal it was considered to have something to hide, and presumed to be a criminal. For this reason, concealed carry was denounced as a detestable practice in the early days of the United States.

> Concealed weapons bans were passed in Kentucky and Louisiana in 1813. (In those days open carry of weapons for self-defense was considered acceptable; concealed carry was denounced as the practice of criminals.) By 1859, Indiana, Tennessee, Virginia, Alabama, and Ohio had followed suit. By the end of the nineteenth century, similar laws were passed in places such as Texas, Florida, and Oklahoma, which protected some gun rights in their state constitutions. Before the mid 1900s, most U.S. states had passed concealed carry laws rather than banning weapons completely. Until the late 1990s, many Southern states were either "No-Issue" or "Restrictive May-Issue". Since then, these states have largely enacted "Shall-Issue" licensing laws, with numerous states legalizing "Unrestricted concealed carry".

See [here](https://en.wikipedia.org/wiki/History_of_concealed_carry_in_the_U.S.){target="_blank"} for more information.

Here are the general categories of Right to Carry Laws:

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC.png"))
```
[source](https://www.nraila.org/gun-laws/){target="_blank"}


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC_map.png"))
```

[source](https://www.nraila.org/gun-laws/){target="_blank"}

You can see that none of the fifty states have no-issue laws currently (the gray category), meaning that all states allow the right to carry firearms at least in some way, however the level of restrictions is dramatically different from one state to another.

Here you can see how these laws have changed over time around the country:
```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics("https://upload.wikimedia.org/wikipedia/commons/thumb/5/5a/Right_to_Carry%2C_timeline.gif/620px-Right_to_Carry%2C_timeline.gif")
```

There is variation from state to state even within the same general category:

For example here are the [current carry laws in Idaho](https://www.nraila.org/gun-laws/state-gun-laws/idaho/) which is considered an "Unrestricted - no permit required" state:

>Idaho permits the open carrying of firearms.

>Idaho law permits both residents and non-residents who are at least 18 years old to carry concealed weapons, without a carry license, outside the limits of or confines of any city, provided the person is not otherwise disqualified from being issued a license to carry.

>A person may also carry concealed weapons on or about his or her person, without a license, in the person’s own place of abode or fixed place of business, on property in which the person has any ownership or leasehold interest, or on private property where the person has permission to carry from any person who has an ownership or leasehold interest in that property. 

>State law also allows any resident of Idaho or a current member of the armed forces of the United States to carry a concealed handgun without a license to carry, provided the person is over 18 years old and not disqualified from being issued a license to carry concealed weapons under state law. An amendment to state law that takes effect on July 1, 2020 changes the reference in the above law from “a resident of Idaho” to “any citizen of the United States.”  


And here are the [current carry laws in Arizona](https://www.nraila.org/gun-laws/state-gun-laws/arizona/) which is also considered an "Unrestricted - no permit required" state:

> Arizona respects the right of law abiding citizens to openly carry a handgun.

> Any person 21 years of age or older, who is not prohibited possessor, may carry a weapon openly or concealed without the need for a license. Any person carrying without a license must acknowledge and comply with the demands of a law enforcement officer when asked if he/she is carrying a concealed deadly weapon, if the officer has initiated an "investigation" such as a traffic stop.

Notice that citizens in Idaho only need to be 18 to carry a firearm, whereas they must be 21 in Arizona. 


In contrast here is an example of [current carry laws in Maryland](https://www.nraila.org/gun-laws/state-gun-laws/maryland/) which is considered a "Rights Restricted-Very Limited Issue" state:

> Carrying and Transportation in Vehicles
It is unlawful for any person without a permit to wear or carry a handgun, openly or concealed, upon or about his person.  It is also unlawful for any person to knowingly transport a handgun in any vehicle traveling on public roads, highways, waterways or airways, or upon roads or parking lots generally used by the public. This does not apply to any person wearing, carrying or transporting a handgun within the confines of real estate owned or leased by him, or on which he resides, or within the confines of a business establishment owned or leased by him.

> Permit To Carry
Application for a permit to carry a handgun is made to the Secretary of State Police. In addition to the printed application form, the applicant should submit a notarized letter stating the reasons why he is applying for a permit.


avocado....Right to carry and covid masks?

# **Limitations**
*** 
There are some important considerations regarding this data analysis to keep in mind: 

1) We do not use all of the data used by either the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} or [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analyses, nor do we perform the same analysis of each article. We instead perform a much simpler analysis with less variables for the purposes of illustration of the concept of multicollinearity and its influence on regression coefficients, not to reproduce either analysis.

2) Our analysis accounts for either the adoption or lack of adoption of a permissive right-to-carry law in each state, but does not acount for differences in the level of permissiveness of the laws.

Recall that these are the categories of right to carry laws:
```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC.png"))
```
```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC_map.png"))
```
States with laws of the category rights restricted - very limited issue (red) are considered as not having a permissive right-to-carry law. Recall that no states currently have a rights infringed/non-issue law.

States of all other categories (shall issue, discretionary/reasonable issue, and no permit required) (all shades of blue) are considered the same in our analysis, as having a permissive right-to-carry law.

3) Because our analysis is an oversimplification, our analysis should not be used for determining policy changes, instead we suggest that users consult with a specialist.


We would also like to note that...AVOCADO
It is important that we do not treat race as an objective measure. Despite this, it can be used to advance scientific inquiry. For more information on this topic, we have included a link to a [paper on the use of race as a measure in epidemiology](https://academic.oup.com/epirev/article/22/2/187/456942). 


We will begin by loading the packages that we will need:

```{r}
library(here)
library(readxl)
library(readr)
library(pdftools)
library(dplyr)
library(magrittr)
library(tidyr)
library(stringr)
library(purrr)
library(forcats)
library(tibble)
library(car) # vif function
library(plm) # fixed effect model, linear regression
library(broom) # tidy output
library(cowplot) # to produce plot of plots 
library(GGally)
library(ggrepel)
library(scales)
library(latex2exp)
library(viridis)
library(ggcorrplot)
library(rsample)

set.seed(999)
```


 Package   | Use                                                                         
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data
[readr](https://readr.tidyverse.org/){target="_blank"}      | to import the CSV file data
[car]  | to calculate vif values
[purrr] | to combine multiple tibbles within a list of tibbles
[forcats] | to collapse levels of factors into more summarised versions
The first time we use a function, we will use the `::` to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.


# **What are the data?**
***

Below is a table from the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} paper that shows the data used in both analyses, where DAW stands for [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} and LM stands for [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Donohue_AppendixJ.png"))
```

We will be using a subset of these variables, which are highlighted in green:


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "ourdata.png"))
```


# **Data Import**
***



## Demographic and population data

To obtain information about age, sex, and race, and overall population we will use US Census Bureau data, just like both of the articles. The cesnus data is available for different time spans. Here are the links for the years used in our analysis. We will use data from 1977 to 2010.

Data   | Link                                                                        
---------- |-------------
**years 1977 to 1979**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1900-1980/state/asrh/)  
**years 1980 to 1989**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1980-1990/counties/asrh/) * county data was used for this decade which also has state information
**years 1990 to 1999**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1990-2000/state/asrh/)
**years 2000 to 2010**  | [link](https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-state.html) <br> [technical documentation](https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/intercensal/state/st-est00int-alldata.pdf){target="_blank"}

To import the data we will use the `read_csv()` function of the `readr` package for the csv files. In some decades, there are separate files for each year, we will read each of these together using the base `list.files()` function to get all of the names for each file and then the `map()` function of the `purrr` package to apply the `read_csv()` function on all of the file paths in the list created by `list.files()`. For years that are txt files we will use `read_table2()` also fo the `readr` package. The `read_table2()` function, unlike the `read_table()`,  allows for any number of whitespace characters between columns, and the lines can be of different lengths.

AVOCADO I am a bit confused about the last decade... it's only one file but it seems to need map...

```{r}

dem_77_79 <- read_csv("docs/Demographics/Decade_1970/pe-19.csv", skip = 5)

dem_80_89 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1980/",
                  pattern = "*.csv",
                  full.names = TRUE) %>% 
  map(~read_csv(., skip=5))

dem_90_99 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1990/",
                  pattern = "*.txt",
                  full.names = TRUE) %>% 
  map(~read_table2(., skip = 14))


dem_00_10_2 <- read_csv("docs/Demographics/Decade_2000/st-est00int-alldata.csv")

dem_00_10 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_2000/",
                  pattern = "*.csv",
                   full.names = TRUE) %>% 
   map(~read_csv(.))

head(dem_00_10)

```

Notice that the `STATE` variable for the demographic data is numeric. That is because it is encoded by [Federal Information Processing Standard (FIPS) state codes](https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code){target="_blank". Thus we also need to import data  about FIPS encoding so that we can identify what data corresponds to what state.


## State FIPS codes



The following data was downloaded from the [US Census Bureau](https://www.census.gov/geographies/reference-files/2014/demo/popest/2014-geocodes-state.html){target="_blank"}.

To import the data we will use the `read_xls()` function of the `readxl` package. Since the first five lines of this excel is information about the source of the data and when it was released, we need to skip importing these lines using the `skip` argument so that the data has the same number of columns for each row. 

```{r, out.width = "500 px"}
knitr::include_graphics(here("img", "FIPS.png"))

```

```{r}
STATE_FIPS <- read_xls("docs/State_FIPS_codes/state-geocodes-v2014.xls", skip = 5)
(STATE_FIPS)
```

## Police staffing data
The following data was downloaded from the [Federal Bureau of Investigation](https://crime-data-explorer.fr.cloud.gov/downloads-and-docs). 


The `read_csv()` function of the `readr` package guesses what the class is for each variable, but sometimes it makes mistakes. It is good to specify the class for variables if you know them. We know that we want the variables about male and female counts to be numeric. We can specify that using the `col_types =` argument. See [here](https://readr.tidyverse.org/articles/readr.html) and [here](https://cran.r-project.org/web/packages/readr/vignettes/readr.html) for more information.

```{r}
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv")
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                    col_types = cols(male_total_ct = "n",
                                     female_total_ct = "n"))

ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                   col_types =  cols(male_total_ct = col_double(),
                                   female_total_ct = col_double()))
head(ps_data)                            
```



## Unemplyment data

The following data was downloaded from the [U.S. Bureau of Labor Statistics](https://data.bls.gov/cgi-bin/dsrv?la). 

There are excel files for each state.  As you can see, there are many rows to skip to make sure that there are the same number of columns for each row. We can also see that the state name is located in a couple of the first rows. 

```{r}
knitr::include_graphics(here("img", "Unemp.png"))
```

We can also see that here if we just try to read in the files directly.

```{r}

ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(.))
      
head(ue_rate_data)[1]
```

So now we will skip the first 10 lines. And also create a names tibble that contains only the cell with the state information.

```{r}
 
 ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(., skip = 10))
  
head(ue_rate_data[1])
```

To get the state name for each file using the `map()` function to perform functions across all of the files, we will specifically import only a small range of cells using the `range = ` argument and then grab the cell that has state information based on it's location within the range of cells imported using `c()` and then use the base `unlist()` function to unlist the list that this creates.

```{r}
ue_rate_names <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>%
  map(~read_xlsx(., range = "B4:B6")) %>%
  map(., c(1,2)) %>%
  unlist()

ue_rate_names
```

Now we will make these values the names of the different tibbles within `ue_rate_data`.
```{r}
names(ue_rate_data) <- ue_rate_names
```

## Poverty data
Extracted from Table 21 from [US Census Bureau Poverty Data ](https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-people.html)

AVOCado strange issue

```{r}

#**persistent warning from unknown origin** https://community.rstudio.com/t/persistent-unknown-or-uninitialised-column-warnings/64879

#solution to above is alledgedly: "In any case the suggested approach is to initialize the column"


poverty_rate_data <- read_xls("docs/Poverty/hstpov21.xls", skip=2) #This may cause initialization issue, not easily reproducible (even after restarting R)

head(poverty_rate_data)
```

We can see that this will require some wranlging to make the data more usable. 

## Violent crime

Violent crime data was obtained from [here](https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm) This data is a bit trickier because of spaces and `/` in the column names, thus the `read_lines()` function of the `readr` package works better than the `read_csv()` function.


```{r}
knitr::include_graphics(here("img", "crime.png"))
```

```{r}
crime_data <- read_lines("docs/Crime/CrimeStatebyState.csv", skip = 2, skip_empty_rows = TRUE)
head(crime_data)

```

We can see that this data will also require some wranlging to make it more usable. 

## Right-to-carry data

This data is extracted from table in [Donohue paper](https://www.nber.org/papers/w23510.pdf) {target="_blank"}. We will use the function `pdf_text()`  of the `pdftools` package to import the pdf document.

```{r}

if(!file.exists(here("docs", "w23510.pdf"))){
  url <- "https://www.nber.org/papers/w23510.pdf"
  utils::download.file(url, here("docs", "w23510.pdf"))
}

DAWpaper <- pdf_text(here("docs", "w23510.pdf"))

head(DAWpaper[1])

```

Again, this data will also require quite a bit of wrangling.



# **Data Wrangling**
***
## State FIPS codes

Let's first take a look at our state FIPS data to see if it needs any cleaning or reshaping. We should start with this data, becuase we will need to use it to wrangle some of the other data.

```{r}
head(STATE_FIPS)
```

We only need the last two columns, but we might want to rename them. The `Name` variable is vague. The variable with the FIPS code is called `State\n(FIPS)`. To get rid of the new line in this variable name and to change the `Name` variable to something more informative, we will use the `rename()` function of the `dplyr` package.  To use this function, we need to list the new name first followed by `=` and then the existing variable. We can rename multiple variables at the same time by using a comma to separate the variables we are renaming. We will use the `select()` function also of the `dplyr` package just to keep these variables, and we will filter out the rows with FIPS values of `00` with the `filter()` function, agian also part of the `dplyr` package. we will specify that we want `STATEFP` values that are not equal to `00` by using this operator: `!=`. We will also use the double pipe operator `%<>%` of the `magrittr` package which allows us to use data as iuput and then reassign it after we peform sum functions using it.

```{r}

STATE_FIPS %<>% 
dplyr::rename( STATEFP = `State\n(FIPS)`,
                 STATE = Name) %>%
    dplyr::select(STATEFP, STATE) %>%
    dplyr::filter(STATEFP != "00")

STATE_FIPS

```

## Demographic and population data


<details><summary> Click here to see detailed information about how the demogrphic data was wrangled </summary>


<font size="6"> **1977-1979**</font>

***


Now let's take a look at our demographic data across the decades that we wish to study. If you have very wide data (meaning it has many columns), one way to view the data so that you can see all of the columns at the same time is to use the `glimpse()` function of the `dplyr` package. 

Taking a look at the first decade of data, we can see that the `Race/Sex Indicator` contains two types of data, the race and the sex. This does not follow the tidy data philosophy, where each cell of a tibble should only contain one piece of information. Typically one might think of using the `separate()` function of the `tidyr` package to split this variable into two. However, one of the race values is `Other races` and since this also has a space, this makes separating this data more tricky.

Instead we will use the `str_extract()` function of the `stringr` package and the `mutate()` function of the `dplyr` package. The "mutate()" will allow us to create new variables, and "str_extract()" function  will allow us to match specific patterns and pull out matches to those patterns. Therefore, if the `Race/Sex Indicator` value is `Other races male` and if we extract patterns matching either `"male"` or `"female"` which we can specify like this `pattern = "male|female"` then, the value will be `male`.

First we need to rename the `Race/Sex Indicator` varaible to not have spaces so that it is compatible with the `str_extract()` function.

We also want to rename a couple of variables to be simpler and filter the data to only include the years of the data we are interested in, as well as remove some variables that we dont need like the `FIPS State Code`. We can remove variables by using the `select()` function with a `-` minus sign in front of the variable we wish to remove.

```{r}
dplyr::glimpse(dem_77_79)


dem_77_79 <- dem_77_79 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select(-`FIPS State Code`, -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`,
        "STATE" = `State Name`) %>%
  filter(YEAR %in% 1977:1979)

glimpse(dem_77_79)
```

That's looking pretty  good! We also want to take all the age group variabels and make one variable that is the age group name and one that is the value of the population count for that age group. To do this we will use the `pivot_longer()` function of the `tidyr` package. To use this function, we need to use the `cols` argument to indicate which columns we want to pivot. We also name the new variables we will create with the `names_to` and `values_to` arguments. The `names_to` will be the name of the variable that will identify each age group and `values_to` will be the name of the variable that contains the corresponding population values.
```{r}
dem_77_79 <- dem_77_79 %>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP")

glimpse(dem_77_79)
```

We also want to get data about the total population for the state for each year.

To do so we can sum all the values for the `SUB_POP` variable that we just created. To do this we can use the `group_by` and `summarise()` functions of the `dplyr` package. The `group_by()` function specifies how we want to calculate  our sum, that we would like to calculate it for each year and each state individually. Thus, all the values that have the same `STATE` and `YEAR` values will be summed together, rather than summing using all of the values in the `SUB_POP` variable. The `.groups` argument allows us to remove the grouping after we peform the calculation with `summarise()`.

```{r}
pop_77_79 <- dem_77_79 %>%
  group_by(YEAR, STATE) %>%
  summarise("TOT_POP" = sum(SUB_POP), .groups = "drop") 

pop_77_79 
```


 Now we will add the population value to the demographic tibble using the `left_join()` function of the `dplyr` package. It is imporant that we specify how this should be done, that the `YEAR` and `STATE` variable vlaues should match eachother. This will place the `dem_77_79` variables to the left of the `pop_77_79` data. 
 
```{r}
dem_77_79 <- dem_77_79 %>%
  left_join(pop_77_79, by = c("YEAR","STATE"))

dem_77_79
```

We will also calculate the percentage that each group makes up of the total population, by dividing the `SUB_POP` by the `TOT_POP` and multiplying by 100 using the `mutate()` function. we will also remove the other population variables.

```{r}
dem_77_79 %<>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  select(-SUB_POP, -TOT_POP)

dem_77_79
```
It is important to make sure that we have the total values we would expect. We have two levels of `SEX`, three levels of `Race`, three levels of `YEAR`, eighteen levels of `AGE_GROUP`, and fifty one levels of `STATE`. If we multiply this together we get 16,524 which is the same as the number of rows in our final `dem_77_79` data. Looks good!

Also Let's make the values of the `SEX` variable capatalized so that they match the other values of the other variables like `RACE` etc. This will help us to keep consistent values across the different years as we wrangle the data for the other decades. To do so we will use the `str_to_title()` function of the `stringr` package. We need to use the `pull()` function to get the values of `SEX` out of `dem_77_79`. Once we make them captialized they are then reasigned to the `SEX` variable. 

```{r}

dem_77_79 %<>%
  mutate(SEX = str_to_title(pull(dem_77_79, SEX)))

# This can also be done line this:
dem_77_79 %<>%
  mutate(SEX = str_to_title(pull(., SEX)))
```

<font size="6"> **1980-1989**</font>

***


For this decade each year is a separate tibble and they are combined as a list.
```{r}
class(dem_80_89)
```

So the first thing we need to do is combine each tibble of the list together. We can do that using the `bind_rows()` function of `dplyr` which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the `map_df()` function of the `purrr` package to allow us to do this across each tibble in our list. 

```{r}
dem_80_89 <- dem_80_89 %>%
  map_df(bind_rows)

glimpse(dem_80_89 )
```

Great! Now our data is all together.

Now we will wrangle the data similarly to the previous decade.
```{r}
dem_80_89 <- dem_80_89 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select( -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`)
         
glimpse(dem_80_89)
```
Notice that this time the state information is based on the numeric FIPS value. We want only the first two values, as the rest indicate the county. We can use the `str_sub()` function of the `stringr` package for this. We will specify that we want to start at the first position and end at the second.  Just like `str_extract()` we need to rename this variable first so that it is compatible. 
```{r}
dem_80_89 %<>%
rename("STATEFP_temp" = "FIPS State and County Codes") %>%
mutate(STATEFP = str_sub(STATEFP_temp, start = 1, end = 2)) %>%
    left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

glimpse(dem_80_89)
```


```{r}
dem_80_89 %<>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP_temp") %>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups="drop")

dem_80_89
```
  
```{r}
pop_80_89 <- dem_80_89 %>%
  group_by(YEAR, STATE) %>%
  summarise("TOT_POP" = sum(SUB_POP), .groups = "drop") 


dem_80_89 <- dem_80_89 %>%
  left_join(pop_80_89, by = c("YEAR","STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_80_89
```

Just like with the data from the 70s we will also change the values for `SEX` to be capitalized.

```{r}
dem_80_89 %<>%
  mutate(SEX = str_to_title(pull(., SEX)))
```

Again, it is important to make sure that we have the total values we would expect. This time we have: two levels of `SEX`, three levels of `Race`, ten levels of `YEAR`, eighteen levels of `AGE_GROUP`, and fifty one levels of `STATE`.

If we multiply these together we get 55,080, which is the same as the number of rows of the final `dem_80_89` data. Looks good!

<font size="6"> **1990-1999**</font>

***

Just like the 80s we need to combine the data across the files:

```{r}
dem_90_99 <- dem_90_99 %>%
  map_df(bind_rows)
```

```{r}
glimpse(dem_90_99)
```
For this decade the column names can't all be imported in a simple way from the table, so they need to be recoded.

Here is what the data looks like before importing:

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "90.png"))
```

So, first using the base `colnames()` function we change the names of the column names.

```{r}

colnames(dem_90_99) <- c("YEAR",
                         "STATEFP",
                         "Age",
                         "NH_W_M",
                         "NH_W_F",
                         "NH_B_M",
                         "NH_B_F",
                         "NH_AIAN_M",
                         "NH_AIAN_F",
                         "NH_API_M",
                         "NH_API_F",
                         "H_W_M",
                         "H_W_F",
                         "H_B_M",
                         "H_B_F",
                         "H_AIAN_M",
                         "H_AIAN_F",
                         "H_API_M",
                         "H_API_F")

glimpse(dem_90_99)
```

Notice also that the first row is all `NA` values from white space in the orginal table for 1990, this is probably true for each year. We can check them dimensions of our table using the base `dim()` function. When we filter for rows where `YEAR` is `NA`, we indeed see 10 rows, which is what we would expect if we have a row like this for each of the years in the decade. We see the same if we try a different variable. Now we will test to see how large our tibble is if we drop rows with `NA` values using the `drop_na()` function of `tidyr`. We that indeed our dimensions only changed by ten, so there are not other rows with missing values that we might not expect. So now we will resign the `dem_90_99` variable after removing these rows.

```{r}

dim(dem_90_99)

dem_90_99 %>%
  filter(is.na(YEAR))

dem_90_99 %>%
  filter(is.na(Age)) 

dem_90_99 %>%drop_na() 

dem_90_99 %<>%drop_na() 
```

Then we sum across the nonhispanic and hispaninc groups because this information is not available for the other previous decades. Then we will remove the variables for the hispanic and nonhispanic subgroups using `select()`.

```{r}

dem_90_99%<>%
    mutate(W_M = NH_W_M + H_W_M,
           W_F = NH_W_F + H_W_F,
           B_M = NH_B_M + H_B_M,
           B_F = NH_B_F + H_B_F,
           AIAN_M = NH_AIAN_M + H_AIAN_M,
           AIAN_F = NH_AIAN_F + H_AIAN_F,
           API_M = NH_API_M + H_API_M,
           API_F = NH_API_F + H_API_F) %>%
  select(-starts_with("NH_"), -starts_with("H_"))

glimpse(dem_90_99)
```

Looking better! We also need to add age groups like the other decades. We will take a look at the 80s data using the `distinct()` function of the `dplyr` package to see what age groups we need. We can use the base `cut()` function to create a new variable with `mutate()` called `AGE_GROUP` that will have a label for every change in 5 years of age. The `right = FALSE` argument specifies that the interval is not closed on the right, meaning that if the value is at the cutpoint like the `Age` value is 5, then it will be in the `5 to 9 years` group.

We can make the labels for the `AGE_GROUP` variable match those of `dem_77_79` but we need to pull out the values of the tibble created by `distinct()`. To do this we can use the `pull()` function from the `dplyr` package. Note that it is important to check that the `AGE_GROUP` values are listed in order for `dem_77_79`. We will also remove the `Age` variable after we create the new `AGE_GROUP` variable for the `dem_90_99` data. 


```{r}

distinct(dem_77_79, AGE_GROUP)
pull(distinct(dem_77_79, AGE_GROUP))

dem_90_99 %<>%
  mutate(AGE_GROUP = cut(Age,
                         breaks = seq(0,90, by=5),
                         right = FALSE, labels = pull(distinct(dem_77_79,AGE_GROUP), AGE_GROUP))) %>%
  select(-Age)

glimpse(dem_90_99)

```

Like the previous decades we will create a `RACE` and `SUB_POP` variable using `pivot_longer()` to create a single `Race` variable out of all the subgroup variables. 

Now we need to collapse the data for the various races so that it matches the previous decades. This time we will use the `case_when()` function of the `dplyr` package and the `str_detect()` function of the `stringr` package to identify when the race is something other than `B` or `W` and replace with the value `Other`. The value to the right of the `~` indicates what we want the value of the new variable to be if the value of the variable we are using with `str_decect()` matches the condition specified. If the value does not match the specified condition, than the other values will be what ever is listed after `TRUE ~`. We will then create population counts as we did previously for the other decades.

Finally, we will create new sums for the subpopulations where we sum across the two `Other` subgroups `Race`  to a create a single value for each value of `YEAR`, `SEX`, `AGE_GROUP`, and `STATE` by using the `group_by()` function and `summarie()`.  

```{r}
dem_90_99  %<>%
  pivot_longer(cols = c(starts_with("W_"),
                    starts_with("B_"),
                    starts_with("AIAN_"),
                    starts_with("API_")),
               names_to = "RACE",
               values_to = "SUB_POP_temp")

dem_90_99 %<>%
  mutate(SEX = case_when(str_detect(RACE, "_M") ~ "Male",
                         TRUE ~ "Female"),
         RACE = case_when(str_detect(RACE, "W_") ~ "White",
                          str_detect(RACE, "B_") ~ "Black",
                          TRUE ~ "Other")) %>%
  left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

dem_90_99 %<>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups="drop")

```

```{r}
pop_90_99 <- dem_90_99 %>%
  group_by(YEAR, STATE) %>%
  summarise(TOT_POP = sum(SUB_POP), .groups = "drop")

dem_90_99 <- dem_90_99 %>%
  left_join(pop_90_99, by=c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_90_99
```


Again, we should check to make sure that we have the total values we would expect. We have the same number of unique values for each of our variables as in with the data from the 80s, so if we collpased the data for the different additional subpopulations in this data, then we have done it correctly. 

Indeed it looks like we have 55,080 rows, which is what we would expect and is the same as the number of rows of the final `dem_80_89` data. Looks good!

<font size="6"> **2000-2010**</font>

***

Again, for this decade we need to combine the data across years.

```{r}
dem_00_10 <- dem_00_10 %>%
  map_df(bind_rows)

glimpse(dem_00_10)

```

Ok, the data looks a bit different from the others. First we will remove a couple of variables that we probably don't need. Also it looks like we have some values for the entire United Sates and we will drop these to be like the other decades.



```{r}
dem_00_10 %<>%
  select(-ESTIMATESBASE2000,-CENSUS2010POP) %>%
  filter(NAME != "United States")
```

We can see that there are lots of values that are zero. According to the [technical documentation](https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/intercensal/state/st-est00int-alldata.pdf){target="_blank"} for this data, zero values indicate the total for the other categories of `Sex`, `Origin`, `Race`, and `AGEGRP`.


```{r, echo = FALSE, out.width = "600 px"}
knitr::include_graphics(here::here("img", "tech_info.png"))
```

So we will drop the total values for `SEX`, `RACE`, and `AGEGRP` by removing the rows where these variables are equal to zero.

We will also want to only select for the total values for `Origin` as we do not wish to divide the data into subgroups about hispanic ethnicity because we do not have that information for the first two decades. Thus we will filter for only the rows where `Origin` is equal to zero.

We will also then remove the `REGION`, `Division`, `STATE`, and `Origin` variables. We will then rename `NAME` to be `STATE` and rename `AGEGRP` to be like the other decades as `AGE_GROUP`.

```{r}
dem_00_10 %<>%
  filter(SEX != 0,
         RACE != 0,
         AGEGRP != 0, 
         ORIGIN == 0) %>%
  dplyr::select(-REGION, -DIVISION, -ORIGIN, -STATE) %>%
  rename("STATE" = NAME,
         "AGE_GROUP" = AGEGRP)

dem_00_10
```


Now we need to recode the numeric values to the values in the techincal documentation. We can do so by adding labels to each numeric level using the base function `factor()`.

```{r}
dem_00_10 %<>%
  mutate(SEX = factor(SEX,
                            levels = 1:2,
                            labels = c("Male",
                                    "Female")),
         RACE = factor(RACE,
                            levels = 1:6,
                            labels = c("White",
                                    "Black",
                                    rep("Other",4))),
         AGE_GROUP = factor(AGE_GROUP,
                            levels = 1:18,
                            labels = pull(distinct(dem_77_79,AGE_GROUP), AGE_GROUP)))
                            
glimpse(dem_00_10)
```

OK, we also want to change the shape of the data so that we have a `YEAR` variable and each estimate of the population is a value in a new variable called `SUB_POP_temp`. 

```{r}
dem_00_10 %<>%
  pivot_longer(cols=contains("ESTIMATE"),
               names_to = "YEAR",
               values_to = "SUB_POP_temp")
```

We will now clean up the `YEAR` variable to only be the numeric value by keeping only the last 4 values of each string using the `str_sub()` function of the `stringr` package.

```{r}
dem_00_10 %<>%
  mutate(YEAR = str_sub(YEAR, start=-4)) %>%
  mutate(YEAR = as.numeric(YEAR))
```


Now we will collapse the data for the different RACES and calculate a new `SUB_POP` value. 

```{r}
dem_00_10 %<>%
  group_by(YEAR, AGE_GROUP, STATE, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups = "drop")
```

Agian, the dimensions look as we expect with 60,588 rows. This time we have two levels of `SEX`, three levels of `Race`, **11** levels of `YEAR`, eighteen levels of `AGE_GROUP`, and fifty one levels of `STATE`. If we multiply this together we get 16,588. Looks good!

Now we will calculate the total polutation and percent of the total as we have done with the previous decades.


```{r}
pop_00_10 <- dem_00_10 %>%
  group_by(YEAR, STATE) %>%
  summarise(TOT_POP = sum(SUB_POP), .groups = "drop")
```

We can also check that our wrangling was performecd correctly by summing the values for the individual subpopulations percentages and seeing if it totals to 100.

```{r}
dem_00_10 %>%
  left_join(pop_00_10, by=c("YEAR", "STATE")) %>%
  group_by(YEAR, STATE) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  summarise(perc_tot = sum(PERC_SUB_POP), .groups = "drop") %>%
  mutate(poss_error = case_when(abs(perc_tot - 100) > 0 ~ TRUE,
                                TRUE ~ FALSE)) %>%
  group_by(poss_error) %>%
  tally()

```

Looks like the percentages for each state for each year all add up to 100, as we would expect. Great! Now we will reasign the `dem_00_10` data with this processing. 

```{r}
dem_00_10 %<>%
  left_join(pop_00_10, by = c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
 select(-SUB_POP, -TOT_POP)

dem_00_10
```

OK, now we are ready to combine all of our demgraphic data together!

<!-- </details> -->

## Combining demographic data

We can check that the colnames are the same for the data for each of the decades by using the `setequal()` function of the `dplyr` package.

```{r}
setequal(colnames(dem_77_79),colnames(dem_80_89))
setequal(colnames(dem_80_89),colnames(dem_90_99))
setequal(colnames(dem_90_99),colnames(dem_00_10))
```


We can also confirm that we have the same number of age groups for each decade by using the base `length()` function. If you did not take a look at the wrangling for the demographic data then you may be unfamiliar with the `pull()` function of the `dplyr` package. This allows you to grab the values of a variable from a tibble. The `distinct()` function which is also of the `dplyr` package creates a tibble of the unique values for a variable.

```{r}
length(pull(distinct(dem_77_79, AGE_GROUP), AGE_GROUP))
length(pull(distinct(dem_80_89, AGE_GROUP), AGE_GROUP))
length(pull(distinct(dem_90_99, AGE_GROUP), AGE_GROUP))
length(pull(distinct(dem_00_10, AGE_GROUP), AGE_GROUP))
```

Looks good!


Now we will combine the data using the `bind_rows()` function of the `dplyr` package. This function appends the data together based on the presence of columns with the same name in the different tibbles.

```{r}
dem <- bind_rows(dem_77_79,
                 dem_80_89,
                 dem_90_99,
                 dem_00_10)
```


```{r}
glimpse(dem)
```

Great! now we have a really large single tibble.

Now we want to select similar demographic data to what was used in the previous analyses.

Here is the table from the [Donohue paper](https://www.nber.org/papers/w23510.pdf){target="_blank"} that compares the data used in the analyses.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2.png'))
```
We can see that only the percentage of males that were from age 15-39 of the race groups (black, white, and other) were used in the Donohue analysis.

Ultimately we intend to make a tibble of data that is similar to each analysis. Therefore, we will create a data tibble about the demogrphaic data for each analysis now.

To do so we will first create a vector of the age groups that should be included in the Donohue-like analysis, that we will call `DONOHUE_AGE_GROUPS`. We will then filter for only the age groups in this vector by using the `filter()` function of the `dplyr` package and the `%in%` operator to indicate that we want to keep all `AGE_GROUP` values that are equal to those within `DONOHUE_AGE_GROUPS`. We also want to filter for only population percentages for males by using the `==` operator. Then we can collpase the age groups from 20-39 by using the `fct_collpase()` function of the `forcats` package.

```{r}
DONOHUE_AGE_GROUPS <- c("15 to 19 years",
                        "20 to 24 years",
                        "25 to 29 years",
                        "30 to 34 years",
                        "35 to 39 years")

dem_DONOHUE <- dem %>%
  filter(AGE_GROUP %in% DONOHUE_AGE_GROUPS,
               SEX == "Male") %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP, "20 to 39 years"=c("20 to 24 years",
                                                                "25 to 29 years",
                                                                "30 to 34 years",
                                                                "35 to 39 years")))

dem_DONOHUE
```

We also want to create a new variable that will contain all the demographic information for each percentage just as was done in the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analysis. This should result in 6 different demographic variables.

To do this we will modify the `AGE_GROUP` variable by using the `mutate()` function of the `dplyr` package. We will replace the spaces in the now two age group categorise with and undesrscore using the `str_replace_all()` function of the `stringr` package which replaces all instances of a pattern in a character string. 

Then we will use the `group_by()` function and the `summarise()` funtion also of the `dplyr` package to allow us to calculate a sum of the percentages for each of the subpopulation percentages for the newly modifed age groups in `AGE_GROUP`. The `.groups = "drop"` argument allows for the grouping to be removed after the `summarise()` function.

```{r}
dem_DONOHUE %<>%
  mutate(AGE_GROUP = str_replace_all(string = AGE_GROUP, 
                                     pattern = " ", 
                                     replacement = "_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop")

dem_DONOHUE
```

Now we will combine the variables `RACE`, `SEX`, and `AGE_GROUP` together into one string separated by underscores using the `unite` function of the `tidyr` package. we will call this new variable `VARIABLE`.
We will rename the `PERC_SUB_POP` variable to be `VALUE` using the `rename()` function of the `dplyr` package. The new name should be listed first before the `=`.

```{r}
dem_DONOHUE %<>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE" = PERC_SUB_POP)

dem_DONOHUE
```

Let's do a quick row number check. We have six different demographic variables, 51 states (DC counts as a state in this case), and 34 different years from 1977 to 2010, we should have 10,404 rows, which we do!

Now, let's do the same for the "Lott-like" analysis.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2.png'))
```

So, in this analysis there were 36 variables covering percentages of indiviuals from 10 to over 65, three  race groups and both males and females. This table is misprinted and does not include the word "Other" for the third race group that was used. 

First we will filter out the age groups that were not included. Then we will collapse the age groups to those that were used by Lott et al. again using the `fct_collpase()` function of the `forcats` package. 

Also we will again combine the values across the variables to create a new demographic varaible with 36 levels. 

```{r}
LOTT_AGE_GROUPS_NULL <- c("Under 5 years",
                          "5 to 9 years")

dem_LOTT <- dem %>%
  filter(!(AGE_GROUP %in% LOTT_AGE_GROUPS_NULL) )%>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP,
                                  "10 to 19 years"=c("10 to 14 years",
                                                     "15 to 19 years"),
                                  "20 to 29 years"=c("20 to 24 years",
                                                     "25 to 29 years"),
                                  "30 to 39 years"=c("30 to 34 years",
                                                     "35 to 39 years"),
                                  "40 to 49 years"=c("40 to 44 years",
                                                     "45 to 49 years"),
                                  "50 to 64 years"=c("50 to 54 years",
                                                     "55 to 59 years",
                                                     "60 to 64 years"),
                                  "65 years and over"=c("65 to 69 years",
                                                        "70 to 74 years",
                                                        "75 to 79 years",
                                                        "80 to 84 years",
                                                        "85 years and over"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)
```

We can indeed check that we have the correct number of levels for `VARIABLE` using the `distinct()` function.

```{r}
 distinct(dem_LOTT, VARIABLE)
```
  
## Combining population Data

We also have population data for each decade that came from wrangling the demogrphic data.

We again want to combine this data, so let's again make sure that all the different tibbles have the same column names.

```{r}
setequal(colnames(pop_77_79),colnames(pop_80_89))
setequal(colnames(pop_80_89),colnames(pop_90_99))
setequal(colnames(pop_90_99),colnames(pop_00_10))

head(pop_77_79)
head(pop_80_89)
head(pop_90_99)
head(pop_00_10)
```

Looks good!

```{r}
population_data <- bind_rows(pop_77_79,
                             pop_80_89,
                             pop_90_99,
                             pop_00_10)

population_data <- population_data %>%
  mutate(VARIABLE = "Population") %>%
  rename("VALUE" = TOT_POP)
```

We could check that we have 51 values for each year by using the `count()` function of the `dplyr` package.

```{r}
population_data %>%
  count(YEAR)
```

## Police staffing

<details><summary> Click here to see details about how the plice staffing data was wrangled. </summary>

OK, now we will wrangle the police staffing data. We want to limit the data to only the years of interest. Then we will also replace NA values with zero for the `male_total_ct` and `female_total_ct` variables using the `replace_na()` function of the `tidyr` packge. We will also, use the `across()` function of the `dplyr` package to select and mutate both of these columns in this way. Since both of these variables have `total_ct` in the name and no other variables do, we can use the `contains()` function of the `dplyr` package to specify that we want to use these columns instead of listing both out.

avocado... why not 2010....

```{r}
glimpse(ps_data)

ps_data %<>%
  filter(data_year >= 1977, 
         data_year <= 2014) %>%
mutate(across(.cols =contains("total_ct"), ~replace_na(., 0)))

glimpse(ps_data)
```

Now we can create a new variable called `officer_total` which will be the sum of these variables. We will then keep just this variable as well as the `data_year`, `pub_agency_name`, and `state_abbr`.

```{r}

ps_data %<>%
  mutate(officer_total = male_total_ct + female_total_ct) %>%
  dplyr::select(data_year,
                pub_agency_name,
                state_abbr,
                officer_total)

ps_data
```

Now we also want to get collapse by `pub_agency_name` to get a total count for each year and each state. So we will do this by using the `group_by()` function and grouping by `data_year` and `state_abbr` and using the `summarise()` function to calculate a sum.

```{r}
ps_data %<>%
  group_by(data_year, state_abbr) %>%
  summarise(officer_state_total=sum(officer_total), .groups = "drop")

ps_data
```
And we will check that we have same number of values (the number of years included in the data) for each state.

```{r}

ps_data %>%
  count(state_abbr)  %>% head()

ps_data %>%
  count(state_abbr) %>%
  filter(n != 38) %>%
  dim()
```
Looks like all the states have 38 values.

Notice also that there are some unusual abrreviations in the `state_abbr` variable.

We will remove data for [US terroitories and associated states]( https://www.fs.fed.us/database/feis/format.html){target="_blank"}  


Abbreviation   | Territory and associated states                                                                    
---------- |-------------
**AS**  | American Samoa 
**GM**  | Guam
**CZ**  | Canal Zone
**FS**  | ??Federated States of Micronesia (usually FM)  
**MP**  |  Northern Mariana Islands
**OT**  | ??U.S. Minor Outlying Islands (usually UM)
**PR**  | Puerto Rico 
**VI**  | Virgin Islands


```{r}

state_of_interest_NULL <- c("AS",
                            "GM",
                            "CZ",
                            "FS",
                            "MP",
                            "OT",
                            "PR",
                            "VI")

ps_data <- ps_data %>%
  filter(!(state_abbr %in% state_of_interest_NULL)) 
```
  
Within the `datasets` package that is loaded with R, there is a dataset called `state` that contains an object called `state.abb` that has the state abbreviations and `state.name` that has the state names.
We will combine these now to add the state names to our data.

```{r}
state_abb_data <- tibble( "state_abbr" = state.abb, "STATE" =state.name)
head(state_abb_data)
```

One unusual thing about this data is that NE is used for Nebraska to avoid confusions with NB in Canada. 
So we want to repace that using the `str_replace()` function of the `stringr` package

```{r}
state_abb_data %<>%
  mutate(state_abbr = str_replace(string = state_abbr, 
                                pattern = "NE", 
                            replacement = "NB"))
```

#### {.scrollable }
```{r}
# Scroll through the output!
state_abb_data %>% print(n = 50)
```
####


We need to add DC to this. We will use the `add_row()` function of `dplyr` to do this.  We just need to specify values for both of the variables.

```{r}
state_abb_data %<>%
  dplyr::add_row(state_abbr = "DC",
                      STATE = "District of Columbia")
```

Now we will add this to our police staffing data and then remove the `state_abbr` variable, so that we just have state names. We will also 

```{r}
ps_data %<>%
  left_join(state_abb_data, by = "state_abbr") %>%
  dplyr::select(-state_abbr)
ps_data
```

Now we will rename the variables to match those of the other datasets.
```{r}
ps_data %<>%
  rename(YEAR = "data_year",
         VALUE = "officer_state_total") %>%
  mutate(VARIABLE = "officer_state_total")
ps_data
```


We also need to adjust the value to be that of every 100,000 people in the state. To do so we need the population for each state, which lukily we already have. We will slightly modify the population data and create a new tibble that will make it more clear how we are dividing by it.

```{r}
denominator_temp <- population_data %>%
 select(-VARIABLE) %>%
  rename("Population_temp"=VALUE)
head(denominator_temp)

ps_data %<>%
  left_join(denominator_temp, by=c("STATE","YEAR"))
head(ps_data)
```

Avocado not sure why the lag?- could use inside plm function...- conceptually I get why you might want to know how staffing has changed recently... but this does not seem to calculate that...hmmmm

```{r}
ps_data %<>%
  mutate(VALUE = (VALUE * 100000) / Population_temp) %>%
  #mutate(VALUE = lag(VALUE)) %>%
  mutate(VARIABLE = "police_per_100k_lag") %>%
  select(-Population_temp)

ps_data
```

<!-- </details> -->



## Unemployment

The first thing we need to do with the unemployment data is combine the data across the different states.
 We can do that using the `bind_rows()` function of `dplyr` which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the `map_df()` function of the `purrr` package to allow us to do this across each tibble in our list. We will then select just the annual data for each state and year and we will rename our variables to be consistent with some of other data that we are working with. Thus we would like our variables to be `YEAR`, `VALUE` and `VARIABLE` in all caps.
 
```{r}

ue_rate_data <- ue_rate_data %>%
  map_df(bind_rows, .id = "STATE")

head(ue_rate_data)

ue_rate_data <- ue_rate_data %>%
  dplyr::select(STATE, Year, Annual) %>%
  rename("YEAR" = Year,
        "VALUE" = Annual) %>%
  mutate(VARIABLE = "Unemployment_rate")

head(ue_rate_data)
```

## Poverty rate



<details><summary> Click here to see details about how the poverty data was wrangled </summary>

OK, now for wrangling the poverty data. First let's take a look at it. 
```{r}
head(poverty_rate_data)
```

We can see that the column names are actually shifted downbelow the row with the year. So we will manually make these values the actual column names.

```{r}
colnames(poverty_rate_data) <- c("STATE",
                                 "Total",
                                 "Number",
                                 "Number_se",
                                 "Percent",
                                 "Percent_se")

poverty_rate_data2 <-poverty_rate_data

```

Let's also remove the rows where the column names are listed, like row number 2.

```{r}
poverty_rate_data  %<>%
  filter(STATE != "STATE")
head(poverty_rate_data)
```

We can also see that there are some extra notes at the end of our data. This is why it is a good idea to look at both the head and tail of your data.

```{r}
tail(poverty_rate_data)
```
We can see that the strings for the state for these rows are very long. We can also see that there are rows that just have the year, where the state is only 4 characters long. We will create a new variable called `length_state` based on the number of characters in the `STATE` values. We will use the `str_length()` function of the `stringr` package. We need to use the `map_dbl()` function to apply this to each row of the `STATE` variable. The `map()` function creates a list, whereas the `map_dbl()` function creates a vector of class double. If we were to use `map()` we would need to use `unlist()` and `pull()`.

```{r}

poverty_rate_data %<>%
 mutate(length_state = map_dbl(STATE, str_length))

# Alternatively with map()
#poverty_rate_data %<>%
#mutate(length_state = unlist(map(pull(poverty_rate_data, STATE), str_length)))

poverty_rate_data
```


Great, now let's look at the tail with our new variable `length_state`
```{r}
tail(pull(poverty_rate_data, length_state))

```

```{r}
poverty_rate_data %<>% 
  filter(length_state <100)

tail(poverty_rate_data)
```
Looks good!

Now let's select all the states that are actually year values to create a new variable about the year. We can do so by using the `str_detect()` function of the `stringr` package to look for digits or values of 0-9. This is indicated by using the `"[:digit:]"`.

As you can see in the [RStudio cheatsheet](https://rstudio.com/resources/cheatsheets/){target="_blank"}  about regular expressions this notation indicates any digit between 0 and 9.

```{r}
knitr::include_graphics(here("img", "regex.png"))
```

#### {.scrollable }

```{r}
# Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  print(n = 51)
```

####

Some of the years (2013 and 2017) are listed twice with a number in parantheses, others are just listed once with a number in parantheses. Looking at the technical documentation, this seems to do with updates to the defition of poverty and to the methods used to estimate poverty levels. See [here](https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-footnotes/cps-historic-footnotes.html){target="_blank"} and [here](https://www2.census.gov/programs-surveys/cps/techdocs/cpsmar19.pdf){target="_blank"} for more information. We will simply select one of the sets of data for 2013 and 2017.

```{r}
poverty_rate_data %>% 
  filter(str_detect(STATE, "2013")) %>%
  filter(str_detect(STATE, "2017"))
```

First let's add the year value to our data. 


There should be consistently data for 51 states (including DC). We can see that sometimes DC is spelled out and sometimes it is not.

#### {.scrollable }
```{r}
### Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  distinct(STATE) %>% print(n = 100)

```
####


Now we will replace `"D.C."` with `"District of Columbia"` using `str_replace()`. We can use the `tally()` function of the `dplyr` package to check that we have fewer now.

```{r}
poverty_rate_data %<>% 
mutate(STATE = str_replace(STATE, pattern = "D.C.", 
                              replacement = "District of Columbia" ))

poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  distinct(STATE) %>% tally()
```
Great! Now are each of the states occurring as often as the unique year values? We can first check how many year values there are. Then can use the `count()` function of the `dplyr` package to check how often the states are repeated.

```{r}

poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  tally()
```

There are 41 different sets of data according to year values.

#### {.scrollable }
```{r}
### Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  count(STATE) %>% 
  print(n = 51)
```
####

Indeed, looks like each of the states are repeated the same number of times!

Now let's create a new variable `YEAR` that repeats the year values for all of the different states and for the row that has just the year value for a total of 52.

```{r}

year_values <- poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  distinct(STATE)

  year_values<-rep(pull(year_values, STATE), each = 52)
setequal(length(year_values), length(poverty_rate_data$STATE))
```

Now we will add this to our `poverty_rate_data`. We will also remove the `length_state` variable using the `select()` function of the `dplyr` package and a minus sign before the variable name.

```{r}
poverty_rate_data %<>%
  mutate(year_value = year_values) %>%
  select(-length_state)
```

#### {.scrollable }
```{r}
#Scroll through the output!
poverty_rate_data %>% print(n = 100)
```
####

Looks good! Now we will remove the rows that have just the year values by only preserving those with alpha characters.

```{r}
poverty_rate_data %<>%
    filter(str_detect(STATE, "[:alpha:]"))

```

Now let's remove the older data for 2013 and 2017 which is the data that appears lower in the tibble.

```{r}
poverty_rate_data %<>%
filter(year_value != "2017") %>%
filter(year_value != "2013 (18)")
```


We also want to just keep the first 4 digits of the year_value and create a `YEAR` variable. We need to pull the `year_value` data because `str_sub()` expects a character vector not a tibble.

```{r}
poverty_rate_data %<>%
  mutate(YEAR = str_sub(pull(., year_value), start = 1, end=4))
```

#### {.scrollable }
```{r}
poverty_rate_data 
```
####

Looks good! Now we will just remove the extra variables and rename the variables we want to keep to be similar to our other data.

```{r}
poverty_rate_data %<>%
  dplyr::select(- Number,
                - Number_se,
                - Percent_se,
                - Total,
                - year_value) %>%
  rename("VALUE" = Percent) %>%
  mutate(VARIABLE = "Poverty_rate",
         YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))
head(poverty_rate_data)
```

Looks great! AVOCADO this data is per 1000k? I think I need to restrict to 2014 to match the other data and thats why I get an error when making the Donohue_df 


From Michael:
```{r, eval = FALSE}

poverty_rate_data <-poverty_rate_data2
notes <- 4

poverty_rate_data2 <- poverty_rate_data[-((dim(poverty_rate_data)[1]-notes+1):dim(poverty_rate_data)[1]),]

states_eq <- 51

extra_col <- 2

rep_rows <- states_eq + extra_col

groups <- (dim(poverty_rate_data)[1])/(rep_rows)

paste(groups - (2018-1980 + 1), "extra groups")

poverty_rate_data$year_group <- rep(1:groups, each=rep_rows)

poverty_rate_data <- poverty_rate_data %>%
  group_by(year_group) %>%
  group_split()

head(poverty_rate_data[[1]])

poverty_rate_data <- poverty_rate_data %>%
  map(~mutate(.,
              row_id = row_number())) %>%
  map(~filter(.,row_id != 2)) %>%
  map(~dplyr::select(.,-row_id))

poverty_rate_data_names <- poverty_rate_data %>%
  sapply(., "[",1,1, drop=TRUE) %>%
  str_replace_all(.,"[:space:]","_")

names(poverty_rate_data) <- poverty_rate_data_names

# Recall 2 extra groups. 
# footnotes available at https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-footnotes/cps-historic-footnotes.html

poverty_rate_data$`2017_(21)` <- NULL

poverty_rate_data$`2013_(19)` <- NULL

poverty_rate_data_names <- poverty_rate_data %>%
  sapply(., "[",1,1, drop=TRUE) %>%
  str_sub(., start = 1, end=4)

names(poverty_rate_data) <- poverty_rate_data_names

poverty_rate_data <- poverty_rate_data %>%
  map_df(bind_rows, .id = "YEAR") %>%
  dplyr::select(-year_group)

poverty_rate_data <- poverty_rate_data %>%
    mutate(n_na = rowSums(is.na(.))) 

# This shows that there is systematic missing values stemmingly *solely* from the rows without poverty data and only a label designating the year
poverty_rate_data %>% 
  group_by(n_na) %>%
  tally()

sapply(poverty_rate_data, class)



colnames(poverty_rate_data)
```

<!-- </details> -->



## Violent crime



<details><summary> Click here to see details about how the violent crime data was wrangled </summary>

The `crime_data` was importated using `read_lines()` and we have some lines that we don't necessarily need. A large amount of the original data is notes at the end of the table. We want to remove these lines. We can determine where they start by searching for the row that contains the first statement of these notes using the `str_which()` function of the `stringr` package. We will subtract one from this as there is a blank line in between.

```{r}
tail(crime_data)
crime_data <- crime_data[-((str_which(crime_data, "The figures shown in this column for the offense of rape were estimated using the legacy UCR definition of rape")-1): length(crime_data))]
#crime_data <- crime_data[-(2143:length(crime_data))]
tail(crime_data)
```

There are lines for each year from 1977 to 2014 as well as four lines about each state and the header information for each state. 
Here you can see what the orginal data looks like:
```{r}
knitr::include_graphics(here("img", "crime_data.png"))
```

We want to delete the header information and only retain the lines numeric values or state names.  Thus since there are 38 years worth of data for each state and 4 lines for each header, then each state has 42 lines. We want to delete the lines between and including line 2 to 4 for each state. We will save the header information once to use later.
```{r}
head(crime_data)
x <- 2014-1977+1
rep_cycle <- 4 + x
rep_cycle_cut <- 2 + x
colnames_crime<-(crime_data[4])
```

So starting at line 2 and and 3 and 4 we create a sequence of numbers that increase by the number of rows of the length of the individual state data. We can do so using the base `seq()` function. We can take a look at these in order using the base `sort()` function.

```{r}
delete_rows <- c(seq(from = 2,
                       to = length(crime_data),
                       by = rep_cycle),
                 seq(from = 3,
                       to = length(crime_data),
                       by = rep_cycle), 
                 seq(from = 4,
                       to = length(crime_data),
                       by = rep_cycle))
sort(delete_rows)

```
Thus we will delete lines 2, 3, and 4 and then skip 40 lines (to account for the state information for the first state, the lines of information for the 38 years, and then the state information for the next state) and then delete the next 3 consecutive lines and so on. We can indeed see that line 44-46 are what we wish to remove.

```{r}
crime_data[44:46]
```

```{r}
crime_data <- crime_data[-delete_rows]
```


Nice!

Now we can select all the lines that have state information. We can repeat each of these for the 38 years for each state as well as this line that contains the state information by using the base `rep()` function with the `each =` argument. Finally we will remove the `"Estimated crime in "` portion of the string using the `str_remove()` function of the `stringr` package.  We will later combine this with the crime data.

```{r}
state_label_order <-crime_data[str_which(crime_data, "Estimated crime")]
state_label_order

state_label_order <- rep(state_label_order, each = 38)

crime_states <-str_remove(state_label_order, pattern = "Estimated crime in ")
head(crime_states)
```

Nice! Now for the rest of the data. We now need to remove the lines with the state information.

```{r}
crime_data <-crime_data[-str_which(crime_data, "Estimated crime")]
head(crime_data)
tail(crime_data)
```

It appears that the data is comma separted with 8 columns. One of the middle columns often has no values, we need to fill these in with NAs. We can use the `read_csv()` fucntion from the `readr` package to do this. It turns out you don't have to have a file, but you can also use a string our a vector.

```{r}
crime_data_sep <-read_csv(crime_data, col_names = FALSE)
head(crime_data)
```
Nice! Now we just need our colnames. Recall that we saved this information. 

```{r}
colnames_crime

colnames(crime_data_sep) <-c("Year",
                             "Population",
                             "Violent_crime_total",
                             "Murder_and_nonnegligent_Manslaughter",
                             "Legacy_rape" ,
                             "Revised_rape", 
                             "Robbery",
                             "Aggravated_assault")
head(crime_data_sep)
```

We also want to combine this with the state information we collected earlier.
We will use the `bind_cols()` function of the `dplyr` package to do this. This requires that the data have the same number of rows.

```{r}
crime_data_sep <-bind_cols(STATE =crime_states, 
          crime_data_sep)

```

Now we will rename the `Viol_crime_count` variable to be `Variable` and we will remove all of the other columns except for `Year`. We will also rename the variables to look like the other datasets.

```{r}
crime_data <- crime_data_sep %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = Violent_crime_total) %>%
  rename("YEAR" = Year) %>%
  select(YEAR,STATE, VARIABLE, VALUE)

crime_data
```




from Michael:

```{r, eval = FALSE}
crime_data2 <- data.frame(cbind(crime_data2, rep(1:(length(crime_data2)/rep_cycle_cut),each=rep_cycle_cut)))

colnames(crime_data) <- c("String","STATE_GROUP")

crime_data <- crime_data %>%
  group_by(STATE_GROUP) %>%
  group_split()

columns_crime_data <- 8

crime_data <- crime_data %>%
  map(~mutate(.,
               State = case_when(str_detect(String, "Estimated crime in ") ~ substring(String, nchar("Estimated crime in ")+1)),
              row_id = row_number())) %>%
  map(~fill(., State)) %>%
  map(~filter(.,row_id > 2)) %>%
  map(~mutate(.,
              String = paste0(String, ",", State))) %>%
  map(~dplyr::select(.,String)) %>%
  map(~str_split_fixed(.$String,",",columns_crime_data + 1)) %>%
  map(~data.frame(.)) %>%
  map(~rename(.,"YEAR"=X1,
              "Extra_col1"=X2,
              "VC"=X3,
              "Extra_col2"=X4,
              "Extra_col3"=X5,
              "Extra_col4"=X6,
              "Extra_col5"=X7,
              "Extra_col6"=X8,
              "STATE"=X9)) %>%
  map(~dplyr::select(.,-contains("Extra_col"))) %>%
  map(~.x %>% mutate_all(~trimws(.,which = "both"))) %>%
  map_df(bind_rows)

sapply(crime_data, class)

crime_data <- crime_data %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = VC) %>%
  as.tibble() %>%
  mutate(YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))
```

<!-- </details> -->

## RTC laws

<details><summary> Click here to see details about how the RTC Law data was wrangled </summary>


The information about the laws for each state are located on page 62 of the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article, so first we will select just this page. We can print part of the character string for this page using the `utils` `str()` function and the `ncar.max` argument.

```{r}
DAWpaper_p_62 <- DAWpaper[[62]]
str(DAWpaper_p_62, nchar.max = 1000)
```

 We can also use the `cat` function to see the data printed nicely to see what we are going for.
```{r}

cat(DAWpaper_p_62)
```


We can see that this is one continuous character string. We can sparate into lines based on the presence of `"\n"` in the string using the `str_split()` function of the `stringr` package. We need to unlist the data first, as the output of `str_split()` is a list. Finally, we can convert it to a tibble using the `as.tibble()` function of the `tibble` package.  We also see that we don't need the first line about the table. We can remove this with the `slice()` function of the `dplyr` package. We can also use this to remove the colnames so that we can replace them. Thus we will use `slice(-(1:2))` to remove the first two lines.

So we will split and unlist() the data.
```{r}
p_62 <- DAWpaper_p_62 %>%
    str_split("\n") %>%
    unlist() %>%
    dplyr::as_tibble() %>%
    slice(-(1:2))

head(p_62)
tail(p_62)

```



We also see by looking at the tail that we want to remove the last two lines. One is empty and the other has only 63 characters, which is the line with the page number.

```{r}
p_62 %<>%
  rename(RTC = value)
p_62 %>%
  mutate(RTC = map_chr(RTC, str_length)) %>%tail()

p_62[53,] # physcial page 60
p_62[54,] # empty line
p_62 %<>%
    slice(-c(53:54))
```

Now we will try splitting by spaces. We can show the output withe the `first()` and `nth()` functions of the `dplyr` package.

```{r}
p_62 %>% pull(RTC) %>% map(str_split, pattern = " ") %>% first()
p_62 %>% pull(RTC) %>% map(str_split, pattern = " ") %>% nth( 5)
```


Interesting, we can see that there are lots of spaces between the elements of the table and that they vary by line. For example there are 6 spaces before Alabama and 7 spaces before Alaska.

Overall, that didn't work quite like we expected. 

Recall from the cheatsheet that `"\\s"` indicates a space. There are also ways to specify how many spaces using curly brackets`{}`. 

```{r}
knitr::include_graphics(here("img", "regex.png"))
knitr::include_graphics(here("img", "quantifiers.png"))
```

The spacing appears to vary quite a bit. WE can use the `str_count()` function of the `stringr` package to see how often we have white spaces larger than 5, 10, 15, or 40 spaces.
```{r}
# how often are there white spaces with more than 5 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{5,}") %>% 
  unlist()
# how often are there white spaces with more than 10 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{10,}") %>%
  unlist()

# how often are there white spaces with more than 15 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{15,}") %>%
  unlist()

# how often are there white spaces with more than 40 spaces
p_62 %>% 
  pull(RTC) %>%
  map(str_count, pattern = "\\s{40,}") %>% 
  unlist()
```

Rows with white spaces with more than 40 consecutive spaces is less common. It appears to be the case in the 1st and 5th row. 

If we take a look at those rows we can see that this occurs when we have a missing value.


```{r}
cat(DAWpaper_p_62)
```


So we will replace white spaces with more than 40 consecutive spaces with `NA`. Let's also remove the leading white spaces that varies infront of the state names, as DC does not have any and this could cause a problem later. We will also replace any white spaces of 2 consecutive spaces or more , but less than 15 white spaces with "|" so that we can split the data based on this symbol. Thus we will also put these around the `NA` value that we are using replace the white spaces made of 40+ spaces. 
```{r}

p_62b <-p_62 %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{40,}", "|N/A|")) %>%
  mutate(RTC =str_trim(pull(., RTC), side = "left")) %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{2,15}", "|"))
head(p_62b)
```

Now anytime there is  one or more `"|"` we should have a column break. So now we will split the data by this symbol.
```{r}

p_62b <-pull(p_62b, RTC) %>%
  str_split( "\\|{1,}") 

head(p_62b)
```

Great! Now we want to put our data in tibble format. To do so we need to bind the rows together. We can do so using the base `rbind()` function. We will use this instead of the `bind_rows()` function of `dplyr` because `rbind()` is less restrictive and allows for columns without names. We will use the base `do.call()` function, so that this is performed along each character string within the list of `p_62b` while maintaining the structure. Then we create a tibble out of this. avocado... describe do.call better... maybe do something tidyverse... 

```{r}
p_62 <- as.tibble(do.call(rbind, p_62b))

colnames(p_62) <- c("STATE",
                    "E_Date_RTC",
                    "Frac_Yr_Eff_Yr_Pass",
                    "RTC_Date_SA")

p_62 <- p_62 %>%
  dplyr::select(STATE, RTC_Date_SA) %>%
  rename("RTC_LAW_YEAR"= RTC_Date_SA) %>%
  mutate(RTC_LAW_YEAR = as.numeric(RTC_LAW_YEAR)) %>%
  mutate(RTC_LAW_YEAR = case_when(RTC_LAW_YEAR == 0 ~ Inf,
                              TRUE ~ RTC_LAW_YEAR))

RTC <-p_62
RTC
```

avocado why inf?

from Michael
```{r, eval = FALSE}

p_62b <-pull(p_62b, RTC) %>%
  str_split( "\\|{1,}") %>% as.data.frame()
  unlist()



p_62b <-p_62 %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{40,}", "|N/A|")) %>%
   mutate(RTC = str_replace_all(pull(., RTC), "\\s{2,15}", "|"))

p_62b %<>%
mutate(value = str_split(pull(., value), "\\|{1}"))

read_csv(p_62b$value)  

p_62 <- p_62 %>%
    apply(1,str_replace_all, "\\s{40,}", "|N/A|") %>%
    str_replace_all("\\s{2,15}", "|") %>%
    as.data.frame()

p_62 <- sapply(p_62$., str_split, "\\|{1,}")

sapply(p_62, nchar)

p_62 <- lapply(p_62, function(x) x[nchar(x) > 0]) 

p_62 <- as.data.frame(do.call(rbind, p_62))

rownames(p_62)

rownames(p_62) <- c()

colnames(p_62) <- c("STATE",
                    "E_Date_RTC",
                    "Frac_Yr_Eff_Yr_Pass",
                    "RTC_Date_SA")
sapply(p_62, class)

p_62 <- p_62 %>%
  dplyr::select(STATE, RTC_Date_SA) %>%
  rename("RTC_LAW_YEAR"=RTC_Date_SA) %>%
  mutate(RTC_LAW_YEAR = as.numeric(RTC_LAW_YEAR)) %>%
  mutate(RTC_LAW_YEAR = case_when(RTC_LAW_YEAR == 0 ~ Inf,
                              TRUE ~ RTC_LAW_YEAR))

sapply(p_62, class)

head(p_62)
```

## Joining Data


Now we will join the data from the different data sets together to create a tibble of data for an analysis that will be similar to the data used by [Donohue et al.](https://www.nber.org/papers/w23510.pdf) {target="_blank"} and [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}.

First we need to check that our data is indeed ready to be joined. We need to make sure that the column names are the same for each dataset that we intend to combine together. 

We will use the `compare_df_cols()` and `compare_df_cols_same()` functions of the janitor package, to ensure that the column names are the same and that the column values are the same type so that the tibbles can be joined by row. 

If they can be joined by row, then `compare_df_cols_same()`  returns the value `TRUE`, while compare_df_cols(), provides a description of the columns.

```{r}
library(janitor)

data_list <-  list(dem_DONOHUE,
                dem_LOTT,
                population_data,
                ue_rate_data,
                poverty_rate_data,
                crime_data,
                ps_data) #police staffing

janitor::compare_df_cols_same(data_list)
janitor::compare_df_cols(data_list)


checkstate <- function(x) { x %<>% distinct(STATE) %>% tally() %>% pull(n) }
map(data_list, checkstate)
checkyear <- function(x) { x %<>% distinct(YEAR) %>% tally() %>% pull(n) }
map(data_list, checkyear)
```

Avocado bind_rows will make na values for the later years beyond 2010 for the datasets that have them... not sure why we are adding these... maybe there is more about that later?

## Donohue, et al.

We will now bind the demogrpahic data that we made for the Donohue-like analysis called `dem_DONOHUE`, as well as all the other datasets that we have wrangled. This is possible because we have the same colnames for each dataset. We will also use the `pivot_wider()` function of the `tidyr` package to change the shape of the data. This will make the data have more columns. Each unique value in the column called `VARIABLE` will be used to make new columns. and the values for each will come from the column called `VALUE`.

```{r}
DONOHUE_DF <- bind_rows(dem_DONOHUE,
                        ue_rate_data,
                        poverty_rate_data,
                        crime_data,
                        population_data,
                        ps_data)
head(DONOHUE_DF)
```


```{r}
DONOHUE_DF %<>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE")

DONOHUE_DF %>%
  slice_sample(n = 10) %>%
  glimpse()
```

We will also add the Right to Carry Law data using the `left_join()` function of the `dplyr` package. Which will place the `DONOHUE_DF` data on the left of the `RTC` data.  Values will be matched by STATE. Then we will create a new variable called `RTC_LAW` using the `mutate()` function and the `case_when()` function of the `dplyr` package  that will have the value `TRUE` if the current year data is equal to or greater than the year that a more premisive RTC law was adopted, otherwise the value will be `FALSE`.

```{r}

head(RTC)

DONOHUE_DF %<>%
  left_join(RTC , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE))

DONOHUE_DF %>%
  slice_sample(n = 10) %>%
  glimpse()
```

Since we have differing numbers of years for each data set, we can use the `drop_na()` function of the `tidyr` package. to remove years that have incomplete data. Thus any row with NA values will be removed.

For example, we can see that for 1977, althouth we have most of the data, we do not have the poverty rate. 
```{r}
DONOHUE_DF %>%
  filter(YEAR == 1977) %>%
  head() %>%
  glimpse()

```

Another example, in 2018 we only have infromation about unemployment rates, poverty rates, and RTC laws.

```{r}
DONOHUE_DF %>%
  filter(YEAR == 2018) %>%
  head() %>%
  glimpse()

```

```{r}
DONOHUE_DF %<>%
 drop_na()

head(DONOHUE_DF) %>% 
  glimpse()
tail(DONOHUE_DF) %>% 
  glimpse()

```

Now we have complete data and the data spans from 1980 to 2010.

```{r}
DONOHUE_DF %>% distinct(YEAR) %>% pull(YEAR)
```

If we include states that had a RTC law adopted before our time span of data, say 1975, then we only have information about crime rates and the other variables of interest after the law was adopted but not before, therefore including these states doesn't really makes sense. Thus, we will drop the data for these states. We can use the `set_diff()` function of the `dplyr` package to see what states are in the `population_data` that contains all the original 51 states (recall this includes the District of Columbia) but are not in the `DONOHUE_DF`. The order matters here. If we did it the other way around with `population_data` listed second, then set_diff would test if there are any states in `Donohue_DF` that are not in `population_data`. As there are none this would result in nothing.

```{r}
baseline_year <- min(DONOHUE_DF$YEAR)
censoring_year <- max(DONOHUE_DF$YEAR)

DONOHUE_DF %<>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

# DONOHUE_DF %<>% 
#   mutate(STATE = as.factor(STATE))
# 
# DONOHUE_DF %>% 
#   pull(STATE) %>% 
#   levels()

setdiff(distinct(population_data, STATE), 
        distinct(DONOHUE_DF, STATE))
```

We will also calculate a violent crime rate relative to the population in that state at that time, now that we have data for both crime count and population.  Will will also calcuate the log value of this rate and the population.

```{r}
DONOHUE_DF %<>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

```

From michael:

```{r, eval = FALSE}
 DONOHUE_DF %>%
   count() %>%
   filter(n != 51) %>%
   print(n=dim(.)[1])

 summary(as.factor(DONOHUE_DF$STATE))

 max(DONOHUE_DF$YEAR) - min(DONOHUE_DF$YEAR) + 1

 DONOHUE_DF <- DONOHUE_DF %>%
   mutate(STATE = fct_collapse(STATE, "District of Columbia"=c("District of Columbia","D.C.")))

 summary(as.factor(DONOHUE_DF$STATE))
  
length(levels(DONOHUE_DF$STATE))

DONOHUE_DF <- DONOHUE_DF %>%
  group_by(STATE, YEAR) %>%
  summarise_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(DONOHUE_DF$STATE)) 

baseline_year <- min(DONOHUE_DF$YEAR)
censoring_year <- max(DONOHUE_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(DONOHUE_DF$STATE)))

length(summary(droplevels(as.factor(DONOHUE_DF$STATE))))

```

## Lott and Mustard

We will now bind the demogrpahic data that we made for the Lott-like analysis called `dem_Lott`, as well as all the other datasets that we have wrangled just as we did for the Donohue-like analysis. Again, this is possible because we have the same colnames for each dataset. 

```{r}
LOTT_DF <- bind_rows(dem_LOTT,
                     ue_rate_data,
                     poverty_rate_data,
                     crime_data,
                     population_data,
                     ps_data) %>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE") %>%
  left_join(RTC , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE)) %>%
   drop_na()


baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

LOTT_DF %<>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

setdiff(distinct(population_data, STATE), 
        distinct(LOTT_DF, STATE))

LOTT_DF %<>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

```
Let's see how the data compares:

We will check the dimensions of each using the base `dim()` function
```{r}
dim(LOTT_DF)
dim(DONOHUE_DF)
```

As expected the `Lott_DF` is 30 columns larger, due to the 30 additional demographic variables. We can check those now as well.

```{r}
LOTT_DF %>%
   colnames()

DONOHUE_DF %>%
   colnames()
```

Lastly, we will check that the `YEAR` values are the same. We can use the `setequal()` function of the `dplyr` package to see if the values are the same. 

```{r}
setequal(DONOHUE_DF %>% distinct(YEAR),
          LOTT_DF %>% distinct(YEAR))
```


Looks as expected! we have 

from Michael:
```{r, eval = FALSE}
LOTT_DF %>%
  group_by(YEAR) %>%
  tally() %>%
  filter(n != 51) %>%
  print(n=dim(.)[1])

summary(as.factor(LOTT_DF$STATE))

max(LOTT_DF$YEAR) - min(LOTT_DF$YEAR) + 1

LOTT_DF <- LOTT_DF %>%
  mutate(STATE = fct_collapse(STATE, "District of Columbia" = c("District of Columbia","D.C.")))

summary(as.factor(LOTT_DF$STATE))
  
length(levels(LOTT_DF$STATE))

LOTT_DF <- LOTT_DF %>%
  group_by(STATE, YEAR) %>%
  summarise_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(LOTT_DF$STATE)) 

baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

LOTT_DF <- LOTT_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

LOTT_DF <- LOTT_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(LOTT_DF$STATE)))

length(summary(droplevels(as.factor(LOTT_DF$STATE))))
```

# **Data Exploration**
***
 Let's do some quick visualizations to get a sense of our outcome of interest, the violent crime data. 
 
First we will plot the rate of violent crime over time to get a sense of the general trend.

To do so we need to summarise the data for each year across all of the states. 
Thus we will use the `group_by()` function and the `summarise()` functions to calculate an overall sum of violent crime relative to the population for each year. 

Then we will use the `ggplot2` package to plot the data. The first step in creating a plot with this package is to use the `ggplot()` function and the `aes()` argument to specify what data should be plotted on the x-axis and what data should be plotted in on the y-axis. Then we select what type of plot we would like to make using one of the `geom_*()` functions. Please see [this case study](https://opencasestudies.github.io/ocs-bp-co2-emissions/){target="_blank"}  for more details.

We can use the `scale_x_continuous()` and `scale_y_continuous()` functions to  modify the axis labels.

The `labs()` function can be used to add labels to the plot, while the `theme()` function allows for manipulation of the detials of the labels, like size and angle. 

All of these functions are part of the `ggplot2` package.
 
```{r}
 
 DONOHUE_DF %>%
  group_by(YEAR) %>%
  summarise(Viol_crime_count = sum(Viol_crime_count),
                  Population = sum(Population),
                    .groups = "drop") %>%
  mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
  ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log)) +
  geom_line() +
  scale_x_continuous(breaks = seq(1980, 2010, by = 1),
                     limits = c(1980, 2010),
                     labels = c(seq(1980, 2010, by = 1))) +
  scale_y_continuous(breaks = seq(5.75, 6.75, by = 0.25),
                     limits = c(5.75, 6.75)) +
  labs(title = "Crime rates fluctuate over time",
       x = "Year",
       y = "ln(violent crimes per 100,000 people)") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 90))
```
Interesting! It appears that there was an overall national peak in violent crime in the early 1990s that has since then declined.  


Now let's take a look at each state.

We will use the `ggrepel` package to add text to the plot using the `geom_text_repel()` function. This is especially useful when there is alot of text, as this function reduces the overlap of text labels. Again see [this case study](https://opencasestudies.github.io/ocs-bp-co2-emissions/){target="_blank"}  for more details on how to add labels to elements of plots.
 
```{r}

DONOHUE_DF %>%
  mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
  ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log, color = STATE)) +
  geom_point(size = 0.5) +
  geom_line(aes(group=STATE),
            size = 0.5,
            show.legend = FALSE) +
  geom_text_repel(data = DONOHUE_DF %>%
              mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
              filter(YEAR == last(YEAR)),
            aes(label = STATE,
                x = YEAR,
                y = Viol_crime_rate_100k_log),
            size = 3,
            alpha = 1,
            nudge_x = 10,
            direction = "y",
            hjust = 1,
            vjust = 1,
            segment.size = 0.25,
            segment.alpha = 0.25,
            force = 1,
            max.iter = 9999) +
  guides(color = FALSE) +
  scale_x_continuous(breaks = seq(1980, 2015, by = 1),
                     limits = c(1980, 2015),
                     labels = c(seq(1980, 2010, by = 1), rep("", 5))) +
  scale_y_continuous(breaks = seq(3.5, 8.5, by = 0.5),
                     limits = c(3.5, 8.5)) +
  labs(title = "States have different levels of crime",
       x = "Year",
       y = "ln(violent crimes per 100,000 people)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))


```

Looks like the crime rages vary quite a bit from one state to another. Some states show increased crime over time while others show decreased crime. 


Now let's take a closer look at some of our other variables.

We can also use the `vis_miss()` function of the `naniar` package to confirm that there are no missing values.

```{r}
DONOHUE_DF %>%
  naniar::vis_miss()

```

Looks like no missing data! 

```{r}
LOTT_DF %>%
  naniar::vis_miss()

```
Same for the `LOTT_DF`.

We can use the `skim()` of the `skimr` package to get a better sense of the data. This also shows missingingness, as well as standard deviations, spread, and means for our data. Also notice that there is a histogram of each variable.

```{r}
skimr::skim(DONOHUE_DF)
skimr::skim(LOTT_DF)

```

# **Data Analysis**
***

## Donohue, et al.

OK! We are now ready to start analyzing our data!. 

We have what is called [panel data](https://en.wikipedia.org/wiki/Panel_data){target="_blank"} . This is a special type of longitundinal data. Longitudinal data are data measurments taken over time. Panel data are data repeatidly measured for for multiple panel members or individuals over time. This is in contrast with time series data, which measures one individual over time and cross sectional data, which measures multiple individuals at one point in time.  In otherwords, panel data is a combination of both, in this case we measure multiple individuals over multiple time periods.  In our case, we have measurements of violent crime and other variables for each state over many years. Therefore we are using measurements about the same states over time.  

In a panel analysis there are $N$ individual panel members and $T$ time points.  

There are two types of panels:  
1. **Balanced** - At each time point ($T$), there are data points for each individual($N$). 

Time Points ($T$)  | Individuals ($N$)                                                                      
---------- |-------------
1977  | Navada 
1977  | Alabama
1977  | Kansas
1978 | Nevada
1978 | Alabama
1978  | Kansas
1979 | Nevada
1979 | Alabama
1979 | Kansas

2. **Unbalanced** - There may be data points missing for some inidividuals ($N$) at some time points ($T$).

Time Points ($T$)  | Individuals ($N$)                                                                         
---------- |-------------
1977  | Navada 
1977  | Alabama
1978 | Nevada
1978 | Alabama
1979 | Nevada
1979 | Alabama
1979 | Kansas


Overall in a a balanced panel, we have $n$ observations, where $n = N*T$.  

In an unbalanced panel, the number of observations is less than $N*T$.


In our case we have:  
$N$ = 45 states (recall that we removed those who had adopted a RTC law before 1980)  
$T$ =  31 years (1980 - 2010)  

In every year we have measurements for each state (as we just saw above), thus our panel is balanced.

So, our total observations $n = 45*31$, thus $n$ = `r 45*31`.  



We will be performing a **panel linear regression model analysis**. 


In such an analysis we will model our data according to this generic model:

$$Y_{it} = a + B'X_{it} + u_{it},\\ u_{it}=\mu _{i}+v_{it}$$
Where $i$ is the individual dimension (in our case individual states) and $t$ is the time dimension.

Some independent variables or regressors $X_{it}$ will vary across individuals and time, while others will be fixed across the time of the study (or don't change over time), while others still will be fixed across individuals but vary across time periods.

$\mu _{i}$ are individual effects that are time independent (aka. fixed over time).  

$v_{it}$ are random effects that vary with time  


There are three general subtypes of [panel regression analysis](https://en.wikipedia.org/wiki/Panel_analysis){target="_blank"}.

Overall, they assume that the different individuals are independent, however the same data for the same individual may be correlated across time.

The main difference between the three subtypes are the assumptions about unobserved differences between individuals.
  
1) **independently pooled panels**  - assumes that there are no individual effects that are independent of time and also no effect of time on all the individuals. In otherwords, the independent variables are not correlated with any error term. This is essentially an ordinary least squares linear regression. This type of panel regression makes the most assumptions and is therefore typically not used for panel data.

2) **fixed effects** - assumes that there are unknown or unobserved unique aspects about the individuals  or heterogeneity among indivudals $a_i$ that are not explained by the independent variables but influence the outcome variable of interest. They do not vary with time or in otherwords are fixed over time but may be **correlated** with independent variables $X_{it}$.  

In this case the intercept can be different for each indivudual ${a_i}$, but the slope is the same across all the individuals.

These individual $a_i$ effects can be correlated with the independent variables $X$.

$$Y_{it} = a_i + B'X_{it} + u_{it},\\ u_{it}=\mu _{i}+v_{it}$$
This type of panel regression makes the least assumptions.


3) **random effects** - assumes that there are unknown or unobserved unique qualities about the individuals that influences the outcome variable of interest that are **not correlated** with the independent variables. 

Thus, the random effects model actually makes **more assumptions** than the fixed effect model.

$$Y_{it} =  B'X_{it} + (a_i + e_{it})}$$
So each indivual has the sampe slope and the same overall error term (a_i + e_{it}). 


See [here](https://www.bauer.uh.edu/rsusmel/phd/ec1-15.pdf) and [here](https://sites.google.com/site/econometricsacademy/econometrics-models/panel-data-models) for more information about these different models.

We will be performing a fixed effect panel regression analysis, as we do in fact think that some of the unobserved qualities about the different states that may be correlated with some of our independent variables. For example, the level of economic opportunity might be an unobserved feature about the states that influences violent crime rate and would be possibly correlated with poverty rate and unemployment. 


avocado:We assume that data for the same individual may be correlated, but we assume that the different indivudals are independent.


avocado from Michael:Some code taken from http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

To perform our analysis we will be using the `plm` package. This stands for Panel Linear Model.

We need to use a special type of data to use this package, called a `pdata.frame` which is short for panel data frame. This allows us to specify that we are using panel data and what the panel structure looks like. 

We need to indicate variable should be used to identify the individuals in our panel, and what variable should be used to identify the time periods in our panel. In our case the `STATE` variable identifies the individuals and the `YEAR` variable identifies the time periods.

We can specify this structure using the `pdata.frame()` function of the `plm` package, by using the `index` argument, where the individual variale is specified first followed by the time variable, like so: `index=c("Individual_Variable_NAME", "Time_Period_Variable_NAME").

```{r}
d_panel_DONOHUE <- pdata.frame(DONOHUE_DF, index=c("STATE", "YEAR"))

class(d_panel_DONOHUE)

slice_head(d_panel_DONOHUE, n = 3)
```

Indeed we have now created a pdata.frame object and we can see that the row names show the individual states and time period years.

OK, now we are ready to run our panel linear model on our panel data frame. 

To do so we will use the `plm()` function and we will specify the formula for our model, where the dependent variable `Viol_crime_rate_1k_log` will be on the left of our `~` sign and all of the independent variables will be listed on the right with `+` signs in between each.

We also need to specify what type of `effect` we would like to model and what type of `model` we would like to use.

There are three main options for the `effect` argument:
1) individual  - model for the effect of indivudal identity
2) time - model for the effect of time
3) twoways - meaning modeling for the effect of both individual identity and time  

There are four main options for the `model` argument:  
1) pooling - standard pooled ordinary least squares regression model  
2) within - fixed effects model (variation between individuals is ignored, model compares individuals to themselves at different periods of time)  
3) between - fixed effects model (variation within individuals from one time point to another is ignored, model compares different individuals at each point of time)  
4) random - random effects (each state has a different intercept but force it to follow a normal distribution - requires more assumptions)

Typically it is best to think about what you are trying to evaluate with your data in trying to choose how to model your data. However, there are also some tests that can help to assess this which we will briefly cover.

We are interested in how violence in each state varied over time, thus we are interested in within `STATE`variation, so we will perform our plm with the `model = within` argument to perform this particular type of fixed effects model. 

We also speculate that there is an effect of individual `STATE` identity and time on violent crime rate. In otherwords, we expect some states to have high rates of crime, and others to have low rates of crime. We also expect crime to change over time. 

If we were to perform this type of analysis we would use the `effect = "twoways"` argument in our `plm()` function like so:

```{r}
DONOHUE_OUTPUT <- plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        effect = "twoways",
                        model = "within",
                        data = d_panel_DONOHUE)
```

To see the results we can use the base `summary()` function. We can view this output in tidy format using the `tidy()` function of the `broom` package.

avocado I think the `analysis` variable is a lable for plots

```{r}
summary(DONOHUE_OUTPUT)

DONOHUE_OUTPUT_TIDY <- tidy(DONOHUE_OUTPUT, conf.int = 0.95)

DONOHUE_OUTPUT_TIDY

DONOHUE_OUTPUT_TIDY$Analysis <- "Analysis 1"
```

We will now perform a test to determine if we could have simply used a pooled model. This test evaluates if the coefficients (including the intercepts) are equal.  To perform this test we will use the `pooltest()` function of the `plm` package to compare the pooled model to the fixed effect within model.

```{r}
pooltest(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag, 
         data = d_panel_DONOHUE, 
         model = "within")

```

The `p-value` is less than a significance threshold of .05, thus we reject the null that our coeffients are all equal. Thus the `within` fixed effects model fit the data better.


We can also perform a test to evaluate if there is indeed an individual effect and a time effect in our model.

We can use the `plmtest()` function of the `plm` package. This performs a Lagrange Multiplier Test. To do so, we need to get the output for a simple pooled model.

```{r}

DONOHUE_pool_model <-plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        model = "pooling",
                        data=d_panel_DONOHUE)

plmtest(DONOHUE_pool_model, effect = c("twoways"))

```

Again, the p-value is much smaller than the significance threshold of < 0.05. Therefore we reject the null that there are no effects, and we can feel confident with proceeding with a twoway effect model.





There is one more test that we could perform. To test if using a random effect model would be more appropriate compared to the fixed effect model, one could use the [Hausmen test](https://en.wikipedia.org/wiki/Durbin%E2%80%93Wu%E2%80%93Hausman_test){target="_blank"} for this (also called the Durbin-Wu-Hausman test). This can be implemented using the `phtest()` function of the `plm` package.

```{r}

DONOHUE_random_model <-  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                      effect = "twoways",
                      model = "random",
                      data=d_panel_DONOHUE)

DONOHUE_within_model <-  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                       effect = "twoways",
                       model = "within",
                       data=d_panel_DONOHUE)

phtest(DONOHUE_within_model, DONOHUE_random_model)

```

This test evaluates if there are errors $u_i$ that are correlated with any of the independent variables. 

We reject the null hypothesis, that there are no inconsistencies, and are confirmed in our plan to used a fixed effects model.



For more information on these tests and this package, see [here](https://cran.r-project.org/web/packages/plm/vignettes/plmPackage.html){target="_blank"}  and [here](http://www.princeton.edu/~otorres/Panel101R.pdf){target="_blank"}.

avocado- stephanie- what do you think about referencing these slides from Princeton?


A final note about the fixed and random effects terminology and how this is slightly different than other definitions:

According to the documentation for the [PLM package](https://cran.r-project.org/web/packages/plm/vignettes/plmPackage.html):
>The fixed/random effects terminology in econometrics is often recognized to be misleading, as both are treated as random variates in modern econometrics (see, e.g., Wooldridge (2002) 10.2.1). It has been recognized since Mundlak’s classic paper (Mundlak (1978)) that the fundamental issue is whether the unobserved effects are correlated with the regressors or not. In this last case, they can safely be left in the error term, and the serial correlation they induce is cared for by means of appropriate GLS transformations. On the contrary, in the case of correlation, “fixed effects” methods such as least squares dummy variables or time-demeaning are needed, which explicitly, although inconsistently27, estimate a group– (or time–) invariant additional parameter for each group (or time period).

>Thus, from the point of view of model specification, having fixed effects in an econometric model has the meaning of allowing the intercept to vary with group, or time, or both, while the other parameters are generally still assumed to be homogeneous. Having random effects means having a group– (or time–, or both) specific component in the error term.

>In the mixed models literature, on the contrary, fixed effect indicates a parameter that is assumed constant, while random effects are parameters that vary randomly around zero according to a joint multivariate normal distribution.


## Lott and Mustard

Ok, now we will do the same for the Lott-like analysis. In this case we would have a very large formula to write. So instead, we can use the `as.formula()` function of the `stats` package and the base `paste()` function to combine all of our explanatory variables into one formula without making a mistake. First we will create an object where we select only for the explanatory variables. 

avocado I tried using glue or dplyr::collapse to make this tidyverse but this didnt work

```{r}
LOTT_variables <- LOTT_DF %>%
  dplyr::select(RTC_LAW,
                contains(c("White","Black","Other")),
                Unemployment_rate,
                Poverty_rate,
                Population_log,
                police_per_100k_lag) %>%
  colnames()


LOTT_fmla <- as.formula(paste("Viol_crime_rate_1k_log ~",
                              paste(LOTT_variables, collapse = " + ")
                              )
                        )

LOTT_fmla

```

That is quite the formula!

Ok, now again we will make a panel data frame and we will perform fit a fixed effect two-way model for time and individuals (`STATE`) with this data as well.

```{r}

d_panel_LOTT <- pdata.frame(LOTT_DF, index=c("STATE", "YEAR"))

LOTT_OUTPUT <- plm(LOTT_fmla,
                      model = "within",
                     effect = "twoways",
                       data = d_panel_LOTT)

summary(LOTT_OUTPUT)

LOTT_OUTPUT_TIDY <- tidy(LOTT_OUTPUT, conf.int = 0.95)

LOTT_OUTPUT_TIDY$Analysis <- "Analysis 2"
```


## RTC coefficient comparison

Now let's make a plot to compare the coeffecient estimate for the Right-to-carry law adoption variable in each model.

First we will combine model fit information for this coefficient for each model.

```{r}
comparing_analyses <- DONOHUE_OUTPUT_TIDY %>%
  bind_rows(LOTT_OUTPUT_TIDY) %>%
  filter(term == "RTC_LAWTRUE")

comparing_analyses
```

We can see that for the first analysis (similar to the [Donohue et al.](https://www.nber.org/papers/w23510.pdf) {target="_blank"} study) the coefficient estimate for the presence of a permissive Right-to-carry law is positive, while for the second analysis (similar to the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} study) the coefficient estimate is negative. Thus in the first analysi we could conclude that the effect of adopting permissive right-to-carry laws  may be associated with increases in violent crime (although this was not a significant result (in contrast with the real [Donohue et al.](https://www.nber.org/papers/w23510.pdf) {target="_blank"} study )); while in the other analysis we could conclude that the laws may be associated with decreases in violent crime.

Let's make a plot of this finding. We will show errorbars for the coefficient estimates for both analyses using the `geom_errorbar()` function of the `ggplot2` package. This requires specifying the minimum and maximum for our errorbar, which in our case we would like to be the low and high values of our confidence intervals for the coefficient estimates. We will also add a horizontal line at y = 0 using the `geom_hline()` function of the `ggplot2` package. 

Finally we will add arrows to emphasize the difference in the direction of the findings using the `geom_segment()` function of the `ggplot2` package. Using the `arrow()` function, we can specify detials about the arrow we would like to add.

```{r}
#library(grid)

comparing_analyses_plot <- ggplot(comparing_analyses) + 
  geom_point(aes(x = Analysis, y = estimate)) +
  geom_errorbar(aes(x = Analysis, ymin = conf.low, ymax = conf.high), width = 0.25) + 
  geom_hline(yintercept = 0, color = "red") +
  scale_y_continuous(breaks = seq(-0.2, 0.2, by = 0.05),
                     labels = seq(-0.2, 0.2, by = 0.05),
                     limits = c(-0.2,0.2)) +
  geom_segment(aes(x = 1, y = 0.125, xend = 1, yend = 0.175),
               arrow = arrow(angle = 45, ends = "last", type = "open"),
               size = 2,
               color = "green",
               lineend = "butt",
               linejoin = "mitre") +
  geom_segment(aes(x = 2, y = -0.125, xend = 2, yend = -0.175),
               arrow = arrow(angle = 45, ends = "last", type = "open"),
               size = 2,
               color = "red",
               lineend = "butt",
               linejoin = "mitre") +
  theme_minimal() + 
  theme(axis.title.x = element_blank(),
        axis.text = element_text(size = 12)) +
  labs(title = "Effect estimate on ln(violent crimes per 100,000 people)",
       y = "Effect estimate (95% CI)")

comparing_analyses_plot
```

We can see that most of the possible range of values for the estimate in analysis 1 are positive, while they are all negative for analysis 2.

# Multicollinearity analysis

How did the above happen?

The analysis dataframes are very similar yet rendered very different results. 

Recall that the only difference is the number of demographic variables. The number of rows or observations is the same. We can use the `all_equal()` function of the `dplyr` package to compare the number of columns of our Donohue-like data and our Lott-like data.

```{r}
all_equal(target = DONOHUE_DF,
          current = LOTT_DF,
          ignore_col_order = TRUE,
          ignore_row_order = TRUE)
```

Using the base `dim()` function we can also look at the number of rows for each and see that the number of observatiosn is the same for both datasets.

```{r}
dim(DONOHUE_DF)[1]
dim(LOTT_DF)[1]
```

The only difference between the two dataframes rests in how the demographic variables were parameterized.

```{r}
DONOHUE_DF %>%
  dplyr::select(contains("years")) %>%
  colnames()

LOTT_DF %>%
  dplyr::select(contains("years")) %>%
  colnames()
```

Clearly, this had an effect on the results of the analysis. 

Let's explore how this occured. 

When seemingly independent variables are highly related to one another, the relationships estimated in an analysis may be distorted. 

In regression analysis, this distortion is often a by-product of a violation of the independence assumption. This distortion, if large enough, can impact statistical inference. 

The phenomona called multicollinearity occurs when independent variables are highly related to one another

There are several ways we can diagnose multicollinearity.

### Correlation

One way we can evaluate the relationships between variables is by examining the correlation between variable pairs.

<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">

It is important to note that multicollinearity and correlation are not one and the same. Correlation can be thought of as the strength of the relationship between variables. On the other hand, multicollinearity can be thought of as the the violation of the independence assumption that is a consequence of this correlation in a regression analysis. 

</div>




#### Scatterplots

One way to look at correlatin across pairs of variables is to use the `ggpairs()` function of the `GGally` package.

```{r}
colnames(DONOHUE_DF)

DONOHUE_DF %>% 
  dplyr::select(RTC_LAW,
                Viol_crime_rate_1k_log,
                Unemployment_rate,
                Poverty_rate,
                Population_log) %>% 
  ggpairs(.,
          columns = c(2:5),
          lower = list(continuous = wrap("smooth_loess",
                                         color = "red",
                                         alpha = 0.5,
                                         size = 0.1)))
```
We can see that for the non-demographic variables, there is very little correlation between the pairs of variables. Only the unemployment rate and the poverty rate show relatively strong correlation, as one might expect. 


#### Heatmaps

Another way to look at correlation if we have many variables is to use heatmaps.

Let's to this now for the deomographic variables for each analysis.

The `ggcorrplot()` function of the `ggpcorplot` package is one way to create such a heatmap.

This requires first calculating the correaltion values using the `cor()` function of the `stats` package.

```{r}
cor_DONOHUE_dem <- cor(DONOHUE_DF %>% dplyr::select(contains("_years")))

corr_mat_DONOHUE <- ggcorrplot(cor_DONOHUE_dem,
           tl.cex = 6,
           hc.order = TRUE,
           colors = c("red",
                      "white",
                      "red"),
           outline.color = "transparent",
           title = "Correlation Matrix, Analysis 1",
           legend.title = TeX("$\\rho$"))

corr_mat_DONOHUE

cor_LOTT_dem <- cor(LOTT_DF %>% dplyr::select(contains("_years")))

corr_mat_LOTT <- ggcorrplot(cor_LOTT_dem,
           tl.cex = 6,
           hc.order = TRUE,
           colors = c("red",
                      "white",
                      "red"),
           outline.color = "transparent",
           title = "Correlation Matrix, Analysis 2",
           legend.title = TeX("$\\rho$"))

corr_mat_LOTT
```
We can see that many of the demographic variables are highly correlated with one another. 


The presence of correlation bewteen variables suggests that we might have multicollinearity. However it does not necessarily mean that we do. So how can we assess this?


### Coefficient estimate instability

One way to look at the possible influence of multicollinearit is to look at the stability of the coefficient estimates.

We will focus on the `RTC_LAW` variable coefficient estimate, as this is of particular interest in our case.

To do so we will perform multiple iterations of our analysis, but we will remove one observation and see if that changes our coefficient estimate results.   


To do this we will use some functions in the `rsample` package which is very useful for splitting data in various ways.

We will use the `loo_cv()` function which stands for leave one out cross validation. This will allow us to split our data into every possible subset where a unique observation is left out of the data.

This function will however only prepare the data to be split.

To actually get the remaining data after the removal of the observation that is left out when need to use a function called `training()`. This is because these functions are often used for in machine learning applications where the data is split between a larger training set and a smaller testing set. Thus we want the larger $n-1$ subset, as opposed to the single value that is removed, (which we could get with the `testing()` function)

avocado move this:Note that this causes our data to be an unbalanced panel.
This does not require any adjustment to the code to model the data, but you will notice that the output will now say "unbalanced".


<details> <summary> Click here to see an example of how this works. </summary>

First we will make a toy dataset that is very simle called test using the `tibble()` function of the `tidyr` package:
```{r}
test <-tidyr::tibble(x = c(1,2,3))
test
```

Now we will use the `loo_cv()` to create leave one out splits:
```{r}
test_samples <- test %>% loo_cv()
test_samples
```

We can take a look at one individual split using the `pull()` function:

```{r}
pull(test_samples, splits)
```

Here you can see that 2 values are intended for the training set (aslo called Analysis set), 1 value is intended for the testing set (also called Assessment set), and 3 values were present initially.

Now we will use the `training()` function to get the data without the obersvation that is set aside. Here is the data for the first subset:
```{r}
training(pull(test_samples, splits)[[1]])
```

Now we will use the `map()` function of `purrr` to get all possible `training` subset of the data.
```{r}

test_subsets <- map(pull(test_samples, splits), training)
test_subsets
```

We can see that there are 3 possible subsets that leave one value out. All 3 possible subsets are created using this method. This method will always create the same number of subsets as there are unqiue values or rows in the data.

</details>


Now we will use this method with the data from our Donohue-like analysis, since this data has `dim(d_panel_DONOHUE)[1]` rows, `dim(d_panel_DONOHUE)[1]` subsets will be created that leave out one row.

First we will create the splits using the `loo_cv()` function:
```{r}
set.seed(124)
DONOHUE_splits <- d_panel_DONOHUE %>% loo_cv()
DONOHUE_splits
```

Now we will use the `training()` function to select the remaining data without the value that was removed for each split:

```{r}
# To get all the data subsets
DONOHUE_subsets <-map(pull(DONOHUE_splits, splits), training)

glimpse(DONOHUE_subsets[[1]])
length(DONOHUE_subsets)
```

As expected the first subset has 1,394 rows and there are 1395 subsets.

Let's see what obsevation was left out in the first subset:

```{r}
d_panel_DONOHUE %>%
  filter(! rownames(d_panel_DONOHUE)  %in% rownames(DONOHUE_subsets[[1]]))

# Another way to check is to use:
DONOHUE_removed <-map(pull(DONOHUE_splits, splits), testing)

DONOHUE_removed[[1]]
```

It looks like the Texas data from 1988 was removed from the first split.


OK, so now let's fit our panel regression on the first subset of data like we did previously:

```{r}
 subset_1_result<-plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        data = DONOHUE_subsets[[1]],
                       index = c("STATE","YEAR"),
                       model = "within",
                      effect = "twoways")
summary(subset_1_result)
```

Indeed, we can see that now we have an unbalanced panel with N = 1394 observations instead of 1395, as expected.

Now that we have our subsets, we want to write a function to fit a panel regression using `plm()`on each subsets. See [this case study](https://opencasestudies.github.io/ocs-bloomberg-vaping-case-study/){target="_blank"}  for more information on writing functions.

avocado add bootstarp description maybe link about cross validation- testing and training sets

```{r}
fit_nls_on_bootstrap_DONOHUE <- function(subset){
                    plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                        data = data.frame(subset),
                       index = c("STATE","YEAR"),
                       model = "within",
                      effect = "twoways")
}

```

Now we can apply this function to each of our subsets simultaneously using the `map()` function of the `purrr` package.  

```{r}

subsets_models_DONOHUE <- map(DONOHUE_subsets, fit_nls_on_bootstrap_DONOHUE)

subsets_models_DONOHUE <- subsets_models_DONOHUE %>%
  map(tidy)

```


Great! Now we want to do the same thing for the Lott data.

```{r}
set.seed(124)
LOTT_splits <- d_panel_LOTT %>% loo_cv()

# To get all the data subsets:
LOTT_subsets <-map(pull(LOTT_splits, splits), training)
```


We need to create a different function to fit the data to account for the larger number of demographic variables. We will use the formula that we made previously.

```{r}
fit_nls_on_bootstrap_LOTT <- function(split){
  plm(LOTT_fmla,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}


subsets_models_LOTT <- map(LOTT_subsets, fit_nls_on_bootstrap_LOTT)

subsets_models_LOTT <- subsets_models_LOTT %>%
  map(tidy)
```

Now we will combine the output so that we can make a plot to visualize the results that we obtained. First let's name each subset that we created.

```{r}
names(subsets_models_DONOHUE) <-     paste0("DONOHUE_",1:length(subsets_models_DONOHUE))

names(subsets_models_LOTT) <- 
paste0("LOTT_",1:length(subsets_models_LOTT))

```

Now we can combine the tibbles within the list of tibbles for the `subsets_models_DONOHUE` and `subsets_models_LOTT` data.

To do this we will use the `bind_rows()` function of the `dplyr` package with the `.id = "ID"` argument, which will create a new variable called `ID` that will list the name of the tibble the data came from.

Then we will combine the data from both the Donohue and Lott simulations.

```{r}

simulations_DONOHUE <- subsets_models_DONOHUE %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 1")

simulations_LOTT <- subsets_models_LOTT %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 2")

simulations <- bind_rows(simulations_DONOHUE,
                         simulations_LOTT)

head(simulations)
tail(simulations)
```

Now we will make a jitter plot using the `geom_jitter()` function of the coefficient estimates of the `RTC_LAWTRUE` variable for each simulation.

```{r}
simulation_plot <- simulations %>%
  filter(term=="RTC_LAWTRUE") %>%
  ggplot(aes(x = Analysis, y = estimate)) + 
  geom_jitter(alpha = 0.25,
              width = 0.1) + 
  labs(title = "Coefficient instability",
       subtitle = "Estimates sensitive to observation deletions",
       x = "Term",
       y = "Coefficient",
       caption = "Results from simulations") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

simulation_plot

```

from Michael
```{r}
sims <- 250

# DONOHUE
samps_DONOHUE <- lapply(rep(dim(DONOHUE_DF)[1]-1, sims),
       function(x)DONOHUE_DF[sample(nrow(DONOHUE_DF),
                                     size = x, replace = FALSE),])

fit_nls_on_bootstrap_DONOHUE <- function(split){
  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_DONOHUE <- lapply(samps_DONOHUE, fit_nls_on_bootstrap_DONOHUE)

samps_models_DONOHUE <- samps_models_DONOHUE %>%
  map(tidy)

names(samps_models_DONOHUE) <- paste0("DONOHUE_",1:length(samps_models_DONOHUE))

simulations_DONOHUE <- samps_models_DONOHUE %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 1")

## LOTT

samps_LOTT <- lapply(rep(round(dim(LOTT_DF)[1]/2), sims),
       function(x) LOTT_DF[sample(nrow(LOTT_DF),
                                  size = x, replace = FALSE),])

fit_nls_on_bootstrap_LOTT <- function(split){
  plm(LOTT_fmla,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_LOTT <- lapply(samps_LOTT, fit_nls_on_bootstrap_LOTT)

samps_models_LOTT <- samps_models_LOTT %>%
  map(tidy)

names(samps_models_LOTT) <- paste0("LOTT_",1:length(samps_models_LOTT))

simulations_LOTT <- samps_models_LOTT %>%
  bind_rows(.id = "Analysis") %>%
  mutate(Analysis = "Analysis 2")

simulations <- bind_rows(simulations_DONOHUE,
                         simulations_LOTT)

simulation_plot <- simulations %>%
  filter(term=="RTC_LAWTRUE") %>%
  ggplot(aes(x = Analysis, y = estimate)) + 
  geom_jitter(alpha = 0.25,
              width = 0.1) + 
  labs(title = "Coefficient instability",
       subtitle = "Estimates sensitive to observation deletions",
       x = "Term",
       y = "Coefficient",
       caption = "Results from simulations") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

simulation_plot
```

### VIF

```{r}
design.matrix <- as.data.frame(model.matrix(DONOHUE_OUTPUT))

design.matrix$Viol_crime_rate_1k_log <- plm::Within(
  d_panel_DONOHUE$Viol_crime_rate_1k_log)

lm_DONOHUE <- lm(Viol_crime_rate_1k_log ~
                        RTC_LAWTRUE + # logical class changes variable name after inital model
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log +
               police_per_100k_lag,
             data = design.matrix)


vif(lm_DONOHUE)

vif_DONOHUE <- vif(lm_DONOHUE)

vif_DONOHUE <- vif_DONOHUE %>%
  as_tibble() %>%
  cbind(., names(vif_DONOHUE)) %>%
  as_tibble()
  
colnames(vif_DONOHUE) <- c("VIF", "Variable")

max_vif_DONOHUE <- max(vif(lm_DONOHUE)) 
```

```{r}
design.matrix <- as.data.frame(model.matrix(LOTT_OUTPUT))

design.matrix$Viol_crime_rate_1k_log <- plm::Within(
  d_panel_LOTT$Viol_crime_rate_1k_log)

LOTT_variables_ols <- LOTT_DF %>%
  dplyr::select(RTC_LAW,
                contains(c("White","Black","Other")),
                Unemployment_rate,
                Poverty_rate,
                Population_log,
                police_per_100k_lag) %>%
  colnames() %>%
  str_replace("RTC_LAW", "RTC_LAWTRUE") # logical class changes variable name after inital model

LOTT_fmla_ols <- as.formula(paste("Viol_crime_rate_1k_log ~",
                              paste(LOTT_variables_ols, collapse = " + ")
                              )
                        )

lm_LOTT <- lm(LOTT_fmla_ols,
             data = design.matrix)

vif(lm_LOTT)

vif_LOTT <- vif(lm_LOTT)

vif_LOTT <- vif_LOTT %>%
  as_tibble() %>%
  cbind(., names(vif_LOTT)) %>%
  as_tibble()
  
colnames(vif_LOTT) <- c("VIF", "Variable")

max_vif_LOTT <- max(vif(lm_LOTT))
```

```{r, echo=FALSE}
#This could be used to label the max VIF of each analysis

max_vif_DONOHUE
max_vif_LOTT
```

$$\frac{1}{1-R_{i}^{2}}$$

```{r}
vif_DONOHUE$Analysis <- "Analysis 1"
vif_LOTT$Analysis <- "Analysis 2"

vif_df <- rbind(vif_DONOHUE,
                vif_LOTT)

vif_plot <- vif_df %>%
  ggplot(aes(x = Analysis, y = VIF)) +
  geom_jitter(width = 0.1, alpha = 0.5, size = 2) +
  geom_hline(yintercept = 10, color = "red") +
  scale_y_continuous(trans = 'log10',
                     limits = c(1,1000)) +
  labs(title = "Variance inflation factors") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

vif_plot
```

# Synthesis

```{r, fig.height=10, echo=FALSE, message=FALSE, warning=FALSE}
title_plots <- ggdraw() + 
  draw_label(
    "Multicollinearity and its effects",
    fontface = 'bold',
    size=18,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

forward <- ggdraw() + 
  draw_label(
    "Analysis 1: 6 demographic variables\nAnalysis 2: 36 demographic variables",
    fontface = 'bold',
    size=10,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

corr_mat_DONOHUE <- ggcorrplot(cor_DONOHUE_dem,
                               tl.cex = 6,
                               hc.order = TRUE,
                               outline.color = "transparent",
                               colors = c("red",
                                          "white",
                                          "red"),
                            legend.title = TeX("$\\rho$")) +
  theme_void() + 
  theme(plot.title= element_text(size=8)) +
  labs(title = "Analysis 1") 

corr_mat_LOTT <- ggcorrplot(cor_LOTT_dem,
                            tl.cex = 6,
                            hc.order = TRUE,
                            outline.color = "transparent",
                            colors = c("red",
                                       "white",
                                       "red"),
                            legend.title = TeX("$\\rho$")) +
  theme_void() + 
  theme(plot.title = element_text(size=8)) +
  labs(title = "Analysis 2") 

plot_A1 <- corr_mat_DONOHUE

plot_A2 <- corr_mat_LOTT

row_A <- plot_grid(plot_A1,
                   plot_A2,
                   nrow = 1)

title_A <- ggdraw() + 
  draw_label(
    "Correlation between variables can induce multicollinearity",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

legend_A <- get_legend(corr_mat_LOTT)

plot_A <- plot_grid(title_A,
                    row_A,
                    ncol = 1,
                    rel_heights = c(0.1,1))

empty_df <- cbind(c(1:10),c(1:10)) %>%
  as.data.frame()

colnames(empty_df) <- c("X", "Y")

plot_B1 <- ggplot(empty_df, aes(x = X, y = Y)) +
  annotate("text",
           x=5,
           y=5,
           label = TeX("$VIF_{i} = \\frac{1}{1-R_{i}^{2}}$"),
           size = 8) +
  theme_void()

plot_B2 <- vif_plot +
  theme(axis.title.x = element_text(size=8))

row_B <- plot_grid(plot_B1,
                       plot_B2,
                       nrow = 1)

title_B <- ggdraw() + 
  draw_label(
    "Variance inflation factors can be used to identify multicollinearity when present",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plot_B <- plot_grid(title_B,
                    row_B,
                    ncol = 1,
                    rel_heights = c(0.1,1))

plot_C1 <- comparing_analyses_plot + 
  theme(axis.text.x = element_text(size = 8),
        axis.title.x = element_blank()) +
  labs(title = "Introduces bias to estimates",
       subtitle = "Bias introduced can change direction of estimate")

plot_C2 <- simulation_plot +
  labs(title = "Reduces precision in estimates")

row_C <- plot_grid(plot_C1,
                       plot_C2,
                       nrow = 1)

title_C <- ggdraw() + 
  draw_label(
    "Multicollinearity can have a negative effect on statistical inference",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plot_C <- plot_grid(title_C,
                    row_C,
                    ncol = 1,
                    rel_heights = c(0.1,1))

plots <- plot_grid(plot_A,
                   plot_B,
                   plot_C,
          ncol = 1,
          rel_heights = c(1,1,1))

mainplot <- plot_grid(title_plots,
                       forward,
                       plots,
                       #legend_uw,
                       ncol = 1,
                       rel_heights = c(0.05,
                                       0.05,
                                       1))

mainplot
```



```{r, echo=FALSE, include=FALSE}
ggsave(here::here("img", "mainplot.png"))
```




# **Data Visualization**
*** 

# **Summary**
*** 

# **Suggested Homework**
*** 

# **Additional Information**
***

## *Helpful Links


[Tidyverse](https://www.tidyverse.org/){target="_blank"}  
[Writing functions](https://r4ds.had.co.nz/functions.html){target="_blank"}   
[Longitudinal studies](https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/7-longitudinal-studies){target="_blank"}   
[Panel data](https://en.wikipedia.org/wiki/Panel_data){target="_blank"}    
[Confidence intervals](https://en.wikipedia.org/wiki/Confidence_interval){target="_blank"}   
[Linear regression model](https://en.wikipedia.org/wiki/Linear_regression){target="_blank"}   

For more information on linear regression see this [book](https://rafalab.github.io/dsbook/linear-models.html#linear-regression-in-the-tidyverse){target="_blank"} and this [case study](https://opencasestudies.github.io/ocs-bp-diet/){target="_blank"}.

avocado fill this out

https://rpubs.com/rslbliss/fixed_effects

http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

https://stats.stackexchange.com/questions/99236/effects-in-panel-models-individual-time-or-twoways

## Acknowledgements

We would like to acknowledge [Daniel Webster](https://www.jhsph.edu/faculty/directory/profile/739/daniel-webster) for assisting in framing the major direction of the case study.

We would also like to acknowledge the [Bloomberg American Health Initiative](https://americanhealth.jhu.edu/) for funding this work. 



Open Case Studies: Examination of Multicollinearity Influence on Inference Using Right-to-Carry Gun Law and Violent Crime Data

Michael Ontiveros, Carrie Wright, PhD.

Motivation

Main Question

Learning Objectives

Context

Limitations

What are the data?

Data Import

Demographic and population data

State FIPS codes

Police staffing data

Unemplyment data

Poverty data

Violent crime

Right-to-carry data

Data Wrangling

State FIPS codes

Demographic and population data

Combining demographic data

Combining population Data

Police staffing

Unemployment

Poverty rate

Violent crime

RTC laws

Joining Data

Donohue, et al.

Lott and Mustard

Data Exploration

Data Analysis

Donohue, et al.

Lott and Mustard

RTC coefficient comparison

Multicollinearity analysis

Correlation

Scatterplots

Heatmaps

Coefficient estimate instability

VIF

Synthesis

Data Visualization

Summary

Suggested Homework

Additional Information

*Helpful Links

Acknowledgements